机器翻译中系统融合技术的研究
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摘要
机器翻译的研究已经有几十年的历史了。目前主流的研究方向是基于统计的机器翻译模型。统计翻译模型从基于词到成熟的基于短语的翻译模型,直至到形式化文法和基于句法的翻译模型,而且加入了很多的特征。因此机器翻译进入了一个“百花齐放,百家争鸣”的时代。但是不同的模型有不同的重排序能力,不同的解码方法有不同的搜索空间。
针对多种机器翻译的翻译结果,我们是否能够融合多种翻译结果来达到“取长补短”,这就是系统融合。系统融合在近几年才取得了标志性的性能提高,但关于系统融合的若干关键问题的研究还不完善。按照融合的粒度来分,可以分为基于句子,基于短语,和基于词的系统融合;按照融合的阶段来说,可以分为后处理阶段和解码阶段的系统融合。本文主要是在两种现今流行的方法中研究:一个是后处理阶段的基于词的系统融合,另一个是解码阶段的系统融合。本文对系统融合中出现的若干关键问题进行了研究。其中包含骨架翻译的选择,对齐方法,在混淆网络中的调序,模型融合后的解码方法等方面。
基于这些问题,本文旨在提高系统融合的性能,针于基于词的系统融合提出了增强的对齐方法,重排序模型以及新的解码框架本;针对模型融合的解码方法进行了深入了研究。本文具体研究内容包括以下几个方面:
1.研究了在基于词一级系统融合中混淆网络的构建中加入了增量的策略。我们也说明增量对齐方法能够有效的改进对齐质量;由于在“翻译错误率”(TER)增量对齐中,假设翻译的顺序影响了增量对齐的结果,我们做了实验性的比较;同时为了解决TER算法只匹配同一词的缺点,我们引入取词根和基于WordNet的词义消歧方法来进行候选翻译和假设翻译中词语的对齐;
2.在系统融合的训练过程中,混淆网络的骨架翻译的选择决定了假设翻译的语序,传统的选择方法是选择具有MBR特性的候选翻译作为对齐的骨架,这就造成了对于混淆网络只考虑到一种语序形式。由于我们融合的机器翻译结果是来自于多种不同文法或是方法,每种翻译结果有着不同的语序,为了使得每种翻译结果的语序都被考虑到,我们构建了一个超级混淆网络,并用两种方法来验证方法的有效性:一种在超级混淆网络中的加入基于混淆网络的特征;另一种是在解码中对多个混淆网络生成的候选翻译翻译进行一致性重打分。实验结果也验证了两种方法的有效性。
3.研究了利用超图进行系统融合的训练和解码。为了计算训练阶段的量值方便,我们在训练的过程引入了二阶半环框架来计算梯度。在解码阶段为了使得原有Cube-Pruning剪枝的框架被保留下来,我们使用Cube Growing,采用了三个阶段的训练解码,第一个阶段是使用Cube Growing进行普通解码;第二个阶段使用n-gram特征来解决伪歧义和一致性解码的问题;第三个阶段对于两个系统融合模型进行融合,三个阶段取得了一定的效果。由于更大的搜索空间和更好整合语言模型特征,得到了比较好的结果。
4.由于每种文法表现能力不同,为了使得每种文法能够取长补短,我们对于层次短语文法和括号转录文法进行在机器翻译的解码框架下进行融合。不同于系统融合方法,解码框架的融合考虑到了解码过程生成的侯选翻译的互相影响,并不是对于生成翻译结果进行重新训练和解码,这样减少了整个过程的时间,同时性能也好于单个系统生成的翻译结果。
综上所述,本文基本上给出了系统融合中的一整套解决方案,有后处理和模型间融合的模型研究,有这些过程的训练和解码研究。为自然语言处理中的难题之一——系统融合,在新的研究思路上进行了初步探索。
Machine translation research at least can date back to a few decades. Currently, the dominant research direction of machine translation is statistical based approaches. The statistical based machine translation have evolved from the classic word-based models, the fairly mature phrase-based models, to formal grammar-based and syntax-based models, and then added more features. Thus, SMT come into a new age of“a hundred flowers blooming and a hundred schools contending”. However, the different models and decoding style have the ability of different reordering and search space.
Facing the translation of multiple translation model, the task of system combination need combine these translations for learning from others' strong points and close the gap. Although system combination make significant performance in recent years, there are many open issues need to be addressed. According to combination grain, system combination includes sentence-based, phrase-based and word-based system. According to combination stage, system combination includes post-processing and decoding system. The major research of the thesis is from two points, which are word-based and post-processing system combination. System combination research has many keys which are skeleton selection, alignment method, reordering, decoding style etc.
To improve the performance of system combination, the thesis has several research aspects, which are enhanced alignment methods, reordering model and new training and decoding framework in word-based system and decoding styles in model combination. Specially, the major contribution of this thesis is as follow:
1. The proposed framework is based on word-level combination with incremental strategy. In order to explain the effective of the method, we use different incremental order in Translation Error Rate (TER) alignment metrics, and experimental result yield the improvement. The order of hypothesis influences the alignment quality. Moreover, to solve the drawback of exact match in TER, we improve the alignment between candidate and hypothesis translation through stemming and WordNet-based WSD.
2. During the training procedure of system combination, the skeleton selection of confusion network decides the order of the hypothesis. The traditional system combination selects hypothesis with MBR as the skeleton, which cause the single order of confusion network. The input hypothesis from various models used by system combination has different reordering. To use various reordering in multiple translation system, we construct a super confusion network, which are multiple confusion network added new confusion-network -based and consensus-based feature, by classic monolingual alignment methods. Then, the experimental results verify the effective in two methods.
3. We investigate hypergraph-based training and decoding in system combination. At training stage, we first introduce the second-order semiring for gradient computation. At decoding stage, we solve the limited search space of N-best decoding. The thesis first presents hypergraph-based decoding method which uses Cube Growing instead of Cube Pruning algorithm. The algorithm makes better performance mostly because of the larger space and the better integration in language model feature. Then, we re-rank hypergrpah by the n-gram feature for solving spurious ambuguity and consensus decoding. Finally, we mix two system combiantion models.
4. Because of the different expression of every grammar, to compensate for drawback, we combine the two types into a unified framework including the hierarchical phrase-based and bracketing transduction grammar. The decoding framework in model combination, which doesn’t re-training and re-decoding stage, is different from system combination. Meanwhile, the performance of model combination is better than individual model.
In brief, basiclly, a complete set of solution methods in system combination has been established, especially the training and decoding methods which explored the hard problem in NLP––system combination on the new research way.
针对多种机器翻译的翻译结果,我们是否能够融合多种翻译结果来达到“取长补短”,这就是系统融合。系统融合在近几年才取得了标志性的性能提高,但关于系统融合的若干关键问题的研究还不完善。按照融合的粒度来分,可以分为基于句子,基于短语,和基于词的系统融合;按照融合的阶段来说,可以分为后处理阶段和解码阶段的系统融合。本文主要是在两种现今流行的方法中研究:一个是后处理阶段的基于词的系统融合,另一个是解码阶段的系统融合。本文对系统融合中出现的若干关键问题进行了研究。其中包含骨架翻译的选择,对齐方法,在混淆网络中的调序,模型融合后的解码方法等方面。
基于这些问题,本文旨在提高系统融合的性能,针于基于词的系统融合提出了增强的对齐方法,重排序模型以及新的解码框架本;针对模型融合的解码方法进行了深入了研究。本文具体研究内容包括以下几个方面:
1.研究了在基于词一级系统融合中混淆网络的构建中加入了增量的策略。我们也说明增量对齐方法能够有效的改进对齐质量;由于在“翻译错误率”(TER)增量对齐中,假设翻译的顺序影响了增量对齐的结果,我们做了实验性的比较;同时为了解决TER算法只匹配同一词的缺点,我们引入取词根和基于WordNet的词义消歧方法来进行候选翻译和假设翻译中词语的对齐;
2.在系统融合的训练过程中,混淆网络的骨架翻译的选择决定了假设翻译的语序,传统的选择方法是选择具有MBR特性的候选翻译作为对齐的骨架,这就造成了对于混淆网络只考虑到一种语序形式。由于我们融合的机器翻译结果是来自于多种不同文法或是方法,每种翻译结果有着不同的语序,为了使得每种翻译结果的语序都被考虑到,我们构建了一个超级混淆网络,并用两种方法来验证方法的有效性:一种在超级混淆网络中的加入基于混淆网络的特征;另一种是在解码中对多个混淆网络生成的候选翻译翻译进行一致性重打分。实验结果也验证了两种方法的有效性。
3.研究了利用超图进行系统融合的训练和解码。为了计算训练阶段的量值方便,我们在训练的过程引入了二阶半环框架来计算梯度。在解码阶段为了使得原有Cube-Pruning剪枝的框架被保留下来,我们使用Cube Growing,采用了三个阶段的训练解码,第一个阶段是使用Cube Growing进行普通解码;第二个阶段使用n-gram特征来解决伪歧义和一致性解码的问题;第三个阶段对于两个系统融合模型进行融合,三个阶段取得了一定的效果。由于更大的搜索空间和更好整合语言模型特征,得到了比较好的结果。
4.由于每种文法表现能力不同,为了使得每种文法能够取长补短,我们对于层次短语文法和括号转录文法进行在机器翻译的解码框架下进行融合。不同于系统融合方法,解码框架的融合考虑到了解码过程生成的侯选翻译的互相影响,并不是对于生成翻译结果进行重新训练和解码,这样减少了整个过程的时间,同时性能也好于单个系统生成的翻译结果。
综上所述,本文基本上给出了系统融合中的一整套解决方案,有后处理和模型间融合的模型研究,有这些过程的训练和解码研究。为自然语言处理中的难题之一——系统融合,在新的研究思路上进行了初步探索。
Machine translation research at least can date back to a few decades. Currently, the dominant research direction of machine translation is statistical based approaches. The statistical based machine translation have evolved from the classic word-based models, the fairly mature phrase-based models, to formal grammar-based and syntax-based models, and then added more features. Thus, SMT come into a new age of“a hundred flowers blooming and a hundred schools contending”. However, the different models and decoding style have the ability of different reordering and search space.
Facing the translation of multiple translation model, the task of system combination need combine these translations for learning from others' strong points and close the gap. Although system combination make significant performance in recent years, there are many open issues need to be addressed. According to combination grain, system combination includes sentence-based, phrase-based and word-based system. According to combination stage, system combination includes post-processing and decoding system. The major research of the thesis is from two points, which are word-based and post-processing system combination. System combination research has many keys which are skeleton selection, alignment method, reordering, decoding style etc.
To improve the performance of system combination, the thesis has several research aspects, which are enhanced alignment methods, reordering model and new training and decoding framework in word-based system and decoding styles in model combination. Specially, the major contribution of this thesis is as follow:
1. The proposed framework is based on word-level combination with incremental strategy. In order to explain the effective of the method, we use different incremental order in Translation Error Rate (TER) alignment metrics, and experimental result yield the improvement. The order of hypothesis influences the alignment quality. Moreover, to solve the drawback of exact match in TER, we improve the alignment between candidate and hypothesis translation through stemming and WordNet-based WSD.
2. During the training procedure of system combination, the skeleton selection of confusion network decides the order of the hypothesis. The traditional system combination selects hypothesis with MBR as the skeleton, which cause the single order of confusion network. The input hypothesis from various models used by system combination has different reordering. To use various reordering in multiple translation system, we construct a super confusion network, which are multiple confusion network added new confusion-network -based and consensus-based feature, by classic monolingual alignment methods. Then, the experimental results verify the effective in two methods.
3. We investigate hypergraph-based training and decoding in system combination. At training stage, we first introduce the second-order semiring for gradient computation. At decoding stage, we solve the limited search space of N-best decoding. The thesis first presents hypergraph-based decoding method which uses Cube Growing instead of Cube Pruning algorithm. The algorithm makes better performance mostly because of the larger space and the better integration in language model feature. Then, we re-rank hypergrpah by the n-gram feature for solving spurious ambuguity and consensus decoding. Finally, we mix two system combiantion models.
4. Because of the different expression of every grammar, to compensate for drawback, we combine the two types into a unified framework including the hierarchical phrase-based and bracketing transduction grammar. The decoding framework in model combination, which doesn’t re-training and re-decoding stage, is different from system combination. Meanwhile, the performance of model combination is better than individual model.
In brief, basiclly, a complete set of solution methods in system combination has been established, especially the training and decoding methods which explored the hard problem in NLP––system combination on the new research way.
引文
1. P. Brown, S. Della Pietra, V. Della Pietra, et al. The Mathematics of Machine Translation: Parameter Estimation. Computational Linguistics. 1993, 19(2):263–311
2. P. Koehn, F. J. Och and D. Marcu. Statistical Phrase based Translation. In Proceedings of the Joint Conference on Human Language Technologies and the Annual Meeting of the North American Chapter of the Association of Computational Linguitics, 2003:48-54.
3. Yang Liu, Yajuan Lü, and Qun Liu. Improving Tree-to-Tree Translation with Packed Forests. In Proceedings of ACL/IJCNLP, 2009: 558-566.
4. R.Frederking,S.Nirenburg.1994. Three heads are better than one.In Proceedings of the Fourth Conference on Applied Natural Language Processing, 1994: 95-100.
5. J. G. Fiscus. A Post-Processing System to Yield Reduced Word Error Rates: Recognizer Output Voting Error Reduction (ROVER) [A]. IEEE Workshop on Automatic Speech Recognition and Understanding[C], 1997: 347-354.
6. Fei Huang, Kishore Papineni. Hierarchical System Combination for Machine Translation. In Proceedings of ACL. 2007: 277-286.
7. Nicola Ueffing, Franz J. Och, Hermann Ney. "Generation of Word Graphs in Statistical Machine Translation". In Proc. Conference on Empirical Methods for Natural Language Processing. 2002:156-163.
8. Michael Collins. Discriminative Reranking for Natural Language Parsing. In Proc. Seventeenth International Conference on Machine Learning (ICML). 2002: 175-182.
9. Jinhua Du and Andy Way. A Three-pass System Combination Framework by Combining Multiple Hypothesis Alignment Methods. In Proceedings of the International Conference on Asian Language Processing (IALP 2009),2009:172-176.
10. Kumar and W. Byrne. Minimum Bayes Risk Decoding for Statistical Machine Translation. In Proc.HI T-NAACI .Boston,M A,USA ,2004:196-176.
11. Antti-Veikko I. Rosti, Necip Fazil Ayan, Bing Xiang, Spyros Matsoukas, Richard Schwartz and Bonnie J.Dorrt. Combing Outputs from Multiple Machine Translation Systems. In: HLT-NAACL 2007. 2007:228-235.
12. Jinhua Du, Yanjun Ma and Andy Way. Source-side Context-informed Hypothesis Alignment for Combining Outputs from Machine Translation Systems. 2009.
13. Mu Li, Duan Nan, Dongdong Zhang, Chi-Ho Li, and Ming Zhou. Collaborative Decoding: Partial Hypothesis Re-Ranking Using Translation Consensus between Decoders. In Proc. of ACL. 2009:585-592.
14. Yang Liu, Haitao Mi, Yang Feng, and Qun Liu. Joint Decoding with Multiple Translation Models. In Proc. of ACL. 2009:576-584.
15. Franz Josef Och. An Efficient Method for Determining Bilingual Word Classes. In Proceedings of EACL. 1999:71–76.
16. Franz Josef Och and Hermman Ney. Discriminitive Training and Maximum Entropy Model for Statistical Machine Translation. In Proceedings of ACL. 2002:295-302
17. Franz Josef Och. Minimum Error Rate Training in Statistical Machine Translation. In: Proc. of the ACL 2003. Sapporo, 2003:160?167.
18. P. Koehn. Statistical Significance Tests for Machine Translation Evaluation. In: Proc. of the EMNLP 2004. Barcelona, 2004:388-395.
19. P. Koehn, H. Hoang, A. Birch, et al. Moses: Open Source Toolkit for Statistical Machine Translation. Annual meeting-association for computational linguistics. 2007, 177-180.
20. Dekai Wu. Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora. Computational Linguistics, 1997,23:377-404.
21. David Chiang. A Hierarchical Phrase-based Model for Statistical Machine Translation. In Proceedings of ACL , Ann Arbor, Michigan. 2005: 263-270
22. David Chiang. Hierarchical phrase-based translation. Comput. Linguist. 2007, 33(2):201–228.
23. Deyi Xiong, Qun liu, and Shouxun Lin. 2006. Maximum Entropy based Phrase Reordering Model for Statistical Machine Translation. In Proceedings of COLING/ACL. Sydney, Australia. 2006: 521-528.
24. Dekai Wu. Stochastic Inversion Transduction Grammars, with Application to Segmentation, Bracketing, and Alignment of Parallel Corpora. In Proc. of the 14th International Joint Conf. on Artificial Intelligence (IJCAI), Montreal.1995:1328– 1334.
25.熊德意.基于括号转录文法和依存文法的统计机器翻译.中国科学院计算技术研究所博士论文.2007.
26. Deyi Xiong, Min Zhang, Ai Ti Aw, Haitao Mi, Qun Liu and Shouxun Lin. Refinements in BTG-based Statistical Machine Translation. In Proceedings of IJCNLP, 2008:505-512.
27. Richard Zens, F.J. Och and H. Ney. Phrase-based Statistical Machine Translation. In Proceedings of the 39th Annual Meeting of the Association of Computational Lingistics. 2002:35-56.
28. I. Dan Melamed. Multitext Grammars and Synchronous Parsers. In Proceedings of HLT/NAACL, 2003:79-86.
29. Deyi Xiong, Min Zhang, Aiti Aw, Haizhou Li. Linguistically Annotated Reordering: Evaluation and Analysis. Computational Linguistics. 2010,36(3):535-568.
30. Min Zhang and Haizhou LI. Tree Kernel-based SVM with Structured Syntactic Knowledge for BTG-based Phrase Reordering.In Proceedings of EMNLP.2009
31. Jinsong Su, Yang Liu, Haitao Mi, Hongmei Zhao, Yajuan Lüand Qun Liu. 2010. Dependency-Based Bracketing Transduction Grammar for Statistical Machine Translation. Beijing, China. In Proceedings of COLING. 2010: 698-707.
32. Andreas Zollmann, Ashish Venugopal. Syntax Augmented Machine Translation via Chart Parsing. In NAACL - Workshop on statistical machine translation, New York. 2006: 4-9.
33. Yuval Marton and Philip Resnik. Soft Syntactic Constraints for Hierarchical Phrased-Based Translation. The 46th Annual Meeting of the association for Computational Linguistics (ACL). Columbus, Ohio. 2008:16-18.
34. L. Shen, J. Xu, R. Weischedel. A New String-to-dependency Machine Translation Algorithm with a Target Dependency Language Model. Proceedings of ACL-08: HLT. 2008:577–585
35. H. Fox. Phrasal Cohesion and Statistical Machine Translation. Proceedings of theACL-02 conference on Empirical methods in natural language processing. 2002:304–3111
36. Michael Collins, Philipp Koehn, Ivona Kucerova. Clause Restructuring for Statistical Machine Translation. In Proceedings of ACL.2005:531-540.
37. Young-Suk Lee , Bing Zhao , Bing Zhao. Clause Reconstructuring for English-to-Japanese Statistical Machine Translation. In Proceedings of EMNLP. 2010.
38. Karthik Visweswariah, Jiri Navratil, Jeffrey Sorensen, Vijil Chenthamarakshan, Nanda Kambhatla. Syntax Based Reordering with Automatically Derived Rules for Improved Statistical Machine Translation. In Proceedings of COLING. 2010: 1119-1127.
39. Chao Wang, Michael Collins, Philipp Koehn. Chinese Syntactic Reordering for Statistical Machine Translation. In Proceedings of EMNLP.2007:.
40. Franz Josef Och, Ignacio Thayer, Daniel Marcu, Kevin Knight, Dragos Stefan Munteanu, Quamrul Tipu, Michel Galley, and Mark Hopkins. Arabic and Chinese MT at USC/ISI. Presentation given at NIST Machine Translation Evaluation Workshop, 2004.
41. Liang Huang, Kevin Knight, and Aravind Joshi. Statistical Syntax-directed Translation with Extended Domain of Locality. In Proceedings of AMTA, Boston, MA.2006:
42. Yang Liu, Qun Liu, and Shouxun Lin. 2006. Tree-to-String Alignment Template for Statistical Machine Translation. In Proceedings of COLING/ACL, pages 609-616, Sydney, Australia, July
43.刘洋.树到串统计翻译模型研究.中国科学院计算技术研究所博士论文.2007.
44. Michel Galley, Mark Hopkins, Kevin Knight, and Daniel Marcu. 2004. What's in a Translation Rule? In Proceedings of HLT/NAACL 2004, pages 273-280, Boston, USA, May.
45. Michel Galley, Jonathan Graehl, Kevin Knight, Daniel Marcu, Steven DeNeefe, Wei Wang; and Ignacio Thayer. 2006. Scalable Inference and Training of Context-rich Syntactic Translation Models. In Proceedings of COLING/ACL 2006, pages 961-968, Sydney, Australia, July.
46. Daniel Marcu, Wei Wang, Abdessamad Echihabi, and Kevin Knight. 2006. SPMT: Statistical Machine Translation with Syntactified Target Language Phrases. In Proceedings of EMNLP 2006, pages 44-52.
47. Kenji Yamada and Kevin Knight. 2001. A Syntax-based Statistical Translation Model. In Proceedings of ACL 2001, pages 523-530.
48. Brooke Cowan, Ivona Ku?erová, and Michael Collins, 2006. A Discriminative Model for Tree-to-Tree Translation. In Proceedings of EMNLP-2006.
49. Brooke Cowan. 2008. A Tree-to-Tree Model for Statistical Machine Translation. PhD Thesis, Massachusetts Institute of Technology.
50. Yuan Ding, Martha Palmer. 2005. Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars. In Proceedings of ACL 2005, pages 541-548, Ann Arbor, Michigan, June.
51. Jason Eisner. 2003. Learning Non-isomorphic Tree Mappings for Machine Translation. Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (Companion Volume), 205-208, Sapporo, July.
52. Chris Quirk, Arul Menezes, and Colin Cherry. 2005. Dependency treelet translation: Syntactically informed phrasal SMT. In Proceedings of ACL 2005, pages 271-279, Ann Arbor, Michigan, June.
53. Min Zhang, Hongfei Jiang, Aiti Aw, Haizhou Li, Chew Lim Tan, and Sheng Li. 2008. A Tree Sequence Alignment-based Tree-to-Tree Translation Model. In Proceedings of HLT:ACL 2008.
54. S. Shieber, Y. Schabes. 1990. Synchronous Tree-adjoining Grammars. Proceedings of the 13th conference on Computational linguistics-Volume 3. 1990:253–258
55. S. Shieber. 2004. Synchronous Grammars as Tree Transducers. Proceedings of the Seventh International Workshop on Tree Adjoining Grammar and Related Formalisms (TAG+ 7). 2004:88–95
56.蒋宏飞.基于同步树替换文法的统计机器翻译研究.哈尔滨工业大学博士论文.2010.
57. Haitao Mi and Liang Huang. 2008. Forest-based Translation Rule Extraction. In Proc. Of EMNLP.
58.何中军.统计机器翻译中短语及结构的译文选择问题研究.中国科学院计算技术研究所博士论文.2008.
59. Zhaopeng Tu, Yang Liu, Young-Sook Hwang, Qun Liu, and Shouxun Lin. 2010. Dependency Forest for Statistical Machine Translation. In Proceedings of COLING 2010, Beijing, China, August.
60. Deyi Xiong, Qun Liu and Shouxun Lin. 2007. A Dependency Treelet String Correspondence Model for Statistical Machine Translation, In Proceedings of Second Workshop on Statistical Machine Translation, Prague, Czech, June 2007
61.刘鹏远.基于知识自动获取的无指导译文消歧方法研究.哈尔滨工业大学博士论文. 2008.
62. M. Carpuat and D. Wu. 2005. Evaluating the Word Sense Disambiguation Performance of Statistical Machine Translation. In Proceedings of the Second International Joint Conference on Natural Language Processing (IJCNLP), Jeju Island, Republic of Korea, 2005:122–127
63. M. Carpuat and D. Wu. 2005. Word sense disambiguation vs. statistical machine translation.43rd ACL Meeting, Ann Arbor. 2005:387~394
64. David Vickrey, Luke Biewald, Marc Teyssier and Daphne Koller. Word-sense Disambiguation for Machine Translation. In Proceedings of the HLT/EMNLP. Vancouver, BC, 2005:771–778
65. Yee Seng Chan, Hwee Tou Ng and David Chiang. Word Sense Disambiguation Improves Statistical Machine Translation. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague, Czech Republic, June 2007:33–40
66. M. Carpuat and D. Wu. Improving Statistical Machine Translation using Word Sense Disambiguation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, June, 2007:61–72
67.王博.机器翻译系统的自动评价及诊断方法研究.哈尔滨工业大学博士论文. 2009.
68. Christoph Tillmann, Stephen Vogel, Hermman Ney, Hassan Sawaf and Alex Zubiaga. Accelerated DP based Search for Statistical Machine Translation. In Proceedings of ACL. 1998.
69. Mathew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla and JohnMakhoul. A Study of Translation Edit Rate with Targeted Human Annotation. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americans. 2006:223-231.
70. Gregor Leusch, Nicola Ueffing and Hermann Ney. Cder:Efficient MT Evaluation Using Block Movement. In the Proceedings of the Thirteenth Conference of the European Chapter of the Association for Computational Linguistics. 2006: 241-248.
71. K. Papineni,S. Roukos,T. Ward,WJ Zhu. BLEU: a Method for Automatic Evaluation of Machine Translation. 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia. 2002:311-318
72. Automatic Evaluation of Machine Translation Quality Using N-gram Co-occurrence Statistics. See:www.nist.gov/speech/tests/mt/doc/n-gramst- udy.pdf . 2003.
73. Chin-Yew Lin and Franz Josef Och. Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics. In Proceedings of ACL. 2004: 605-618.
74. Joseph P. Turian, Luke Shen, and I. Dan Melamed. 2003. Evaluation of Machine Translation and Its Evaluation. Proceedings of MT Summit. 2003: 386-393.
75. Satanjeev Banerjee and Alon Lavie. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. Proceedings of the ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization. 2005:65-73.
76. J. Albrecht and R. Hwa. Regression for Sentence-Level MT Evaluation with Pseudo References. In Proceedings of ACL. 2007:296.
77. K. Duh. Ranking vs. Regression in Machine Translation Evaluation. In Proceeding of the Third Workshop on Statistical Machine Translation. 2008: 191-194.
78. Maja Popovic and Hermann Ney. Word Error Rate: Decomposition over POS Classes and Application for Error Analysis. In Proceedings of the Second Workshop on Statistical Machine Translation. 2007.
79. Ming Zhou, Bo Wang, Shujie Liu, Mu Li, Dongdong Zhang, Tiejun Zhao. Diagnostic Evaluation of Machine Translation Systems Using AutomaticallyConstructed Linguistic Check-Points. In: Proc. of 22nd Coling. 2008: 1121-1128.
80. Bo Wang, Tie Jun Zhao, Mu Yun Yang, Hong Fei Jiang, and Sheng Li. Stability vs. Effectiveness: Improved Sentence-level Combination of Machine Translation based on Weighted MBR. In: Proceedings of the International Conference on Asian Language Processing. Singapore, Singapore: IEEE. 2009:39-42
81. S. Bangalore, G. Bordel and G. Riccardi. Computing Consensus Translation from Multiple Machine Translation Systems. In Workshop on Automatic Speech Recognition and Understanding, Madonna di Campiglio, Italy. 2001:351-354.
82. Evgeny Matusov, Nicola Ueffing and Hermann Ney. Computing Consensus Translation from Multiple Machine Translation Systems using Enhanced Hypothesis Alignment. In Proceedings of EACL. 2006:33-40.
83. Jinhua Du and Andy Way. Using TERp to Augment the System Combination for SMT. In Proceedings of the Ninth Conference of the Association for Machine Translation AMTA. 2010:.
84. Xiaodong He, Mei Yang, Jianfeng Gao, Patrick Nguyen, and Robert Moore. Indirect-HMM based Hypothesis Alignment for Combining Outputs from Machine Translation Systems. In Proc. of EMNLP. 2008:98-107.
85. Xiaodong He, Kristina Toutanova. Joint Optimization for Machine Translation System Combination. In Proc. of EMNLP. 2009: 1202-1211.
86. Damianos Karakos, Jason Eisner, Sanjeev Khudanpur, and Markus Dreyer. Machine Translation System Combination using ITG-based Alignments. In Proceedings of ACL. 2008: 81-84.
87. Chi-Ho Li, Xiaodong He, Yupeng Liu and Ning Xi. Incremental HMM Alignment for MT System Combination. In Proceedings of ACL. 2009: 949-957.
88. Antti-Veikko I. Rosti, Bing Zhang, Spyros Matsoukas, and Richard Schwartz. Incremental Hypothesis Alignment for Building Confusion Networks with Application to Machine Translation System Combination.In Proceedings of the 3rd ACL Workshop on SMT. 2008: 183-186.
89. Yang Feng, Yang Liu, Haitong Mi, Qun Liu, Yajuan Lu. Lattice-based System Combination for Statistical Machine Translation. In Proceedings of ACL. 2009: 1105-1113.
90. E. Matusov, G. Leusch, R. E. Banchs, N. Bertoldi, D. Dechelotte, M. Paulik, S.Roukos, H. Schwenk and H. Ney. System Combination for Machine Translation of Spoken and Written Language. IEEE Trnasactions on Audio. Speech and Language Processing. 2008: 1222-1237.
91. Yong Zhao and Xiaodong He. Using N-gram based Features for Machine Translation System Combination. NAACL-HLT. 2009.
92. Boxing Chen, Min Zhang, Haizhou Li and Aiti Aw. A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination. In: Proceeding of ACL. 2009: 941-948.
93. Wolfgang Macherey,Franz Josef Och. An Empirical Study on Computing Consensus Translations from Multiple Machine Translation Systems. In Proceedings of ACL. 2007: 986-995.
94. Hongfei Jiang, Muyun Yang, Tiejun Zhao, Sheng Li, Bo Wang. A Statistical Machine Translation Model based on a Synthetic Synchronous Grammar. In Proc. of ACL. 2009: 125-128.
95. Nan Duan, Mu Li, Dongdong Zhang and Ming Zhou. Mixture Model-based Minimum Bayes Risk Decoding using Multiple Machine Translation Systems. COLING. 2010: 313-321.
96. Nan Duan, Mu Li, Tong Xiao and Ming Zhou. The Feature Subspace Method for SMT System Combination. In Proc. of EMNLP 2009:1096-1104.
97. Tong Xiao, Jingbo Zhu, Muhua Zhu and Huizhen Wang. Boosting-based System Combination for Machine Tranlsation. In Proc. of the 48th Annual Meeting of the Association for Computational Linguistics (ACL). Uppsala, Sweden. 2010.
98. Liang Huang and David Chiang. 2005. Better k-best Parsing. In Proceedings of the International Workshop on Parsing Technologies (IWPT), pages 53–64.
99. Liang Huang. Forest reranking: Discriminative parsing with Non-local Features. In Proc. Of ACL/HLT. 2008.
100. Liang Huang and Kenji Sagae. Dynamic Programming for Linear-time Incremental Parsing. In Proceedings of ACL. 2010:1077-1086.
101. Hao Zhang and Daniel Gildea. Efficient Multi-pass Decoding for Synchronous Context Free Grammars. In Proceedings of ACL, Columbus. 2008.
102. Zhifei Li, Jason Eisner and Sanjeev Khudanpur. Variational Decoding for Statistical Machine Translation. In Proceedings of ACL. 2009:593-601.
103. Zhifei Li and Jason Eisner. First- and Second-order Expectation Semirings with Applications to Minimum-Risk Training on Translation Forests. In Proceedings of EMNLP. 2009:40-51.
104. Shankar Kumar, Wolfgang Macherey, Chris Dyer, Franz Och. Ef?cient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices, In Proceedings of ACL. 2009:163-171.
105. Zhifei Li and Sanjeev Khudanpur. Efficient Extraction of Oracle-best Translations from Hypergraphs. In Proceedings of NAACL.2009:9-12.
106. Hao Xiong, Haitao Mi, Yang Liu, and Qun Liu. Forest-based Semantic Role Labeling. In Proceedings of AAAI. 2010.
107. John DeNero, Shankar Kumar, Ciprian Chelba, and Franz Och. Model Combination for Machine Translation. In proceedings of NAACL. 2010: 975-983.
108. Nan Duan, Mu Li, Dongdong Zhang and Ming Zhou. Mixture Model-based Minimum Bayes Risk Decoding using Multiple Machine Translation Systems. In Proc. of COLING. 2010:313-320.
109. Nicola Ueffing, Hermann Ney. Word-Level Confidence Estimation for Machine Translation using Phrase-Based Translation Models. In Proceedings of HLT/EMNLP. 2005:763-770.
110. Nicola Ueffing, Hermann Ney. Word-Level Confidence Estimation for Machine Translation. Computational Linguistics. 2007, 33(1): 9-40.
111. Matthew Snover, Nitin Madnani, Bonnie J.Dorr and Richard Schwartz. Fluency, adequacy, or HTER? Exploring Different Human Judgments with a Tunable MT Metric. In Proceedings of the WMT. 2009:259–268.
112. Necip Fazil Ayan, Jing Zheng and Wen Wang. Improving Alignments for Better Confusion Networks for Combining Machine Translation Systems. In Proceedings of the Coling.2008:33–40.
113. David Kauchak and Regina Barzilay. Paraphrasing for Automatic Evaluation. In Proceedings of the HLT-NAACL. 2006:455–462.
114. Liang Zhou, Chon-Yew Lin and Eduard Hovy. Re-evaluating Machine Translation Results with Paraphrase Support. In Proceedings of the EMNLP. 2006:77–84.
115. Deyi Xiong, Min Zhang, Haizhou Li. Error Detection for Statistical Machine Translation using Linguistic Features. In Proceedings of ACL. 2010:.
116. S Jayaraman, A lavin. Multi-Engine Machine Translation Guided by Explicit Word Matching.In Proc. of ACL.2005:101-104.
117. Khe Chai Sim, William J. Byrne, Mark J.F. Gales, Hichem Sahbi, and Phil C. Woodland.Consensus Network Decoding for Statistical Machine Translation System Combination. In Proc. of ICASSP.2007:105-108.
118. Chi-Ho Li, Dongdong Zhang, Mu Li, Ming Zhou, Chi-Ho Li, Minghui Li,Yi Guan. A Probabilistic Approach to Syntax-based Reordering for Statistical Machine Translation. In Proceedings of ACL. 2007:720.
119. Roger Levy and Christopher Manning. Is It Harder To Parse Chinese, or The Chinese Treebank? Published in Proceedings of ACL. 2003: 439-446.
120. Jianfeng Gao, Mu Li, Andi Wu and Chang-Ning Huang. Chinese Word Segmentation And Named Entity Recognition: A Pragmatic Approach. Computational Linguistics.2005,31(4):531-574.
121. George Foster and Roland Kuhn. Mixture-Model Adaptation for SMT. In Proc. of the Second ACL Workshop on Statistical Machine Translation. 2007:128-136.
122. Wen Wang, Andreas Stolcke, Jing Zheng. Reranking Machine Translation Hypotheses with Structured and Web-based Language Models. In Proc. of IEEE ASRU. 2007:159– 164.
123. Arul Menezes and Chris Quirk. Using Dependency Order Templates to Improve Generality in Translation. In Proc 2nd WMT at ACL, Prague, Czech Republic.2007:1-8.
124. Galen Andrew. A Hybrid Markov/semi-Markov Conditional Random Field for Sequence Segmentation. In Proceedings of EMNLP. Sydney, Australia. 2006: 465-472.
125. Simon Corston-Oliver, Anthony Aue, Kevin Duh, amd Eric Ringger. Multilingual Dependency Parsing using Bayes Point Machines, Proc. of NAACL-HLT, New York. 2006:160-167.
126. Xiaodong He. Using Word-Dependent Transition Models in HMM based Word Alignment for Statistical Machine Translation. In Proc 2nd WMT at ACLPrague, Czech Republic.2007:80-87.
127. Patrick Nguyen, Jianfeng Gao and Milind Mahajan. SRILM: a Scalable Language Modeling Toolkit. Microsoft Research Technical Report MSR-TR-2007-144. 2007.
128. Robert Moore and Chris Quirk. 2007. Faster Beam-Search Decoding for Phrasal Statistical Machine Translation. In Proc. of MT Summit XI.
129. Miller, G. A, Beckwith, R., Fellbaum, C., Gross, D., and Miller, K. Introduction to WordNet: An on-line Lexical Database. In Five Papers on WordNet, CSL report, Cognitive Science Laboratory, Princeton University. International Journal of Lexicography. 1990, 3(4):235~244
130.姚天顺,张俐,高竹. WordNet综述.语言文字应用. 2001, 1:27~32
131. Z. Wu, M. Palmer. Verb Semantics and Lexical Selection. In the Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics. Las Cruces, New Mexico, 1994:133-138.
132. Jia Xu, Richard Zens, Hermann Ney. Sentence Segmentation using IBM Word Alignment Model 1. In: Proceedings of the 10th Annual Conference of the European Association for Machine Translation. Budapest, Hungary: Association for Computational Linguistics, 2005:280-287
133. Jinhua Du and Andy Way. An Incremental Three-pass System Combination Framework by Combining Multiple Hypothesis Alignment Methods. International Journal of Asian Language Processing. 2010,20(1):1--15.
134. Franz Josef Och and H. Ney. A Systematic Comparison of Various Statistical Alignment Models. Computational Linguistics. 2003,29(1):19–51.
135. Antti-Veikko I. Rosti, Spyros Matsoukas, and Richard Schwartz. Improved Word-level System Combination for Machine Translation. In Proceedings of ACL. 2007,45(1):312.
136. Roy Tromble, Shankar Kumar, Franz Och and Wolfgang Macherey. Lattice Minimum Bayes-Risk Decoding for Statistical Machine Translation. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing,. 2008:620-629.
137. Abhishek Arun, Barry Haddow, and Philipp Koehn. A Unified Approach to Minimum Risk Training and Decoding. In Proceedings of 5th Workshop onStatistical Machine Translation. 2010:365-374.
138. Adam Pauls, John DeNero, and Dan Klein. Consensus Training for Consensus Decoding in Machine Translation. In Proceedings of EMNLP. 2009:.
139. John DeNero, David Chiang, and Kevin Knight. Fast Consensus Decoding over Translation Forest. In proceedings of ACL. 2009:567-575.
140. Adam Pauls and Dan Klein. Hierarchical Search for Parsing. In Proceedings of NAACL. 2009:557-565.
141. Chirstopher Dyer, Smaranda Muresan, Philip Resnik. Generalizing Word Lattice Translation. In Proceedings of ACL. 2008.
142. Graeme Blackwood. Lattice rescoring methods for statistical machine translation. Ph.D. Thesis. Cambridge University Engineering Department and Clare College. 2010.
143. Joshua Goodman. Semiring parsing. Computational Linguistics, 25, 1999:573-605.
144. Liang Huang. Advanced Dynamic Programming in Semiring and Hypergraph Frameworks. In COLING, Manchester, UK. 2008:.
145. David Chiang, Yuval Marton, and Philip Resnik. Online large-margin training of syntactic and structural translation features. In Proceedings of the Con ference on Empirical Methods in Natural Language Processing. 2008:224-233.
146. Ng, Andrew. Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proceedings of the Twenty-first International Conference on Machine Learning. 2004:.
147. David A. Smith and Jason Eisner. Minimum Risk Annealing for Training Log-linear Models. In Proceedings of COLING-ACL. 2006:787–794.
148. J. Nocedal. 1980. Updating quasi-Newton Matrices with Limited Storage. Math. Comput. 35 773-782
149. Liang Huang and David Chiang. Forest Rescoring: Faster Decoding with Integrated Language Models. In Proceedings of ACL, Prague, Czech Rep. 2007,45(1):144.
150. Boxing Chen, Min Zhang, Aiti Aw and Haizhou Li. Regenerating Hypotheses for Statistical Machine Translation. In: Proceeding of COLING. Manchester, UK. 2008:05-112
151. Le Zhang. Maximum Entropy Modeling Toolkit for Python and C++. Available at http://homepages.inf.ed.ac.uk/s0450736 /maxent toolkit.html. 2004.
152. A. Berger, S. Della Pietra, and V. Della Pietra. A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics. 1996:22(1):39-71,.
153. A. Stolcke. SRILM: An Extensible Language Modeling Toolkit. In: Proc. of the ICSLP 2002. Colorado, 2002:901-904.
154. R. Kneser, H. Ney. Improved Backing-off for M-gram Language Modeling. In: Proc. of the ICASSP. Detroit, 1995:181-184.
155. Y Zhang, S Vogel, A Waibel. Interpreting BLEU/NIST Scores: How Much Improvement do We Need to Have a Better System. In: Proc. of the LREC .2004:2051-2054.
2. P. Koehn, F. J. Och and D. Marcu. Statistical Phrase based Translation. In Proceedings of the Joint Conference on Human Language Technologies and the Annual Meeting of the North American Chapter of the Association of Computational Linguitics, 2003:48-54.
3. Yang Liu, Yajuan Lü, and Qun Liu. Improving Tree-to-Tree Translation with Packed Forests. In Proceedings of ACL/IJCNLP, 2009: 558-566.
4. R.Frederking,S.Nirenburg.1994. Three heads are better than one.In Proceedings of the Fourth Conference on Applied Natural Language Processing, 1994: 95-100.
5. J. G. Fiscus. A Post-Processing System to Yield Reduced Word Error Rates: Recognizer Output Voting Error Reduction (ROVER) [A]. IEEE Workshop on Automatic Speech Recognition and Understanding[C], 1997: 347-354.
6. Fei Huang, Kishore Papineni. Hierarchical System Combination for Machine Translation. In Proceedings of ACL. 2007: 277-286.
7. Nicola Ueffing, Franz J. Och, Hermann Ney. "Generation of Word Graphs in Statistical Machine Translation". In Proc. Conference on Empirical Methods for Natural Language Processing. 2002:156-163.
8. Michael Collins. Discriminative Reranking for Natural Language Parsing. In Proc. Seventeenth International Conference on Machine Learning (ICML). 2002: 175-182.
9. Jinhua Du and Andy Way. A Three-pass System Combination Framework by Combining Multiple Hypothesis Alignment Methods. In Proceedings of the International Conference on Asian Language Processing (IALP 2009),2009:172-176.
10. Kumar and W. Byrne. Minimum Bayes Risk Decoding for Statistical Machine Translation. In Proc.HI T-NAACI .Boston,M A,USA ,2004:196-176.
11. Antti-Veikko I. Rosti, Necip Fazil Ayan, Bing Xiang, Spyros Matsoukas, Richard Schwartz and Bonnie J.Dorrt. Combing Outputs from Multiple Machine Translation Systems. In: HLT-NAACL 2007. 2007:228-235.
12. Jinhua Du, Yanjun Ma and Andy Way. Source-side Context-informed Hypothesis Alignment for Combining Outputs from Machine Translation Systems. 2009.
13. Mu Li, Duan Nan, Dongdong Zhang, Chi-Ho Li, and Ming Zhou. Collaborative Decoding: Partial Hypothesis Re-Ranking Using Translation Consensus between Decoders. In Proc. of ACL. 2009:585-592.
14. Yang Liu, Haitao Mi, Yang Feng, and Qun Liu. Joint Decoding with Multiple Translation Models. In Proc. of ACL. 2009:576-584.
15. Franz Josef Och. An Efficient Method for Determining Bilingual Word Classes. In Proceedings of EACL. 1999:71–76.
16. Franz Josef Och and Hermman Ney. Discriminitive Training and Maximum Entropy Model for Statistical Machine Translation. In Proceedings of ACL. 2002:295-302
17. Franz Josef Och. Minimum Error Rate Training in Statistical Machine Translation. In: Proc. of the ACL 2003. Sapporo, 2003:160?167.
18. P. Koehn. Statistical Significance Tests for Machine Translation Evaluation. In: Proc. of the EMNLP 2004. Barcelona, 2004:388-395.
19. P. Koehn, H. Hoang, A. Birch, et al. Moses: Open Source Toolkit for Statistical Machine Translation. Annual meeting-association for computational linguistics. 2007, 177-180.
20. Dekai Wu. Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora. Computational Linguistics, 1997,23:377-404.
21. David Chiang. A Hierarchical Phrase-based Model for Statistical Machine Translation. In Proceedings of ACL , Ann Arbor, Michigan. 2005: 263-270
22. David Chiang. Hierarchical phrase-based translation. Comput. Linguist. 2007, 33(2):201–228.
23. Deyi Xiong, Qun liu, and Shouxun Lin. 2006. Maximum Entropy based Phrase Reordering Model for Statistical Machine Translation. In Proceedings of COLING/ACL. Sydney, Australia. 2006: 521-528.
24. Dekai Wu. Stochastic Inversion Transduction Grammars, with Application to Segmentation, Bracketing, and Alignment of Parallel Corpora. In Proc. of the 14th International Joint Conf. on Artificial Intelligence (IJCAI), Montreal.1995:1328– 1334.
25.熊德意.基于括号转录文法和依存文法的统计机器翻译.中国科学院计算技术研究所博士论文.2007.
26. Deyi Xiong, Min Zhang, Ai Ti Aw, Haitao Mi, Qun Liu and Shouxun Lin. Refinements in BTG-based Statistical Machine Translation. In Proceedings of IJCNLP, 2008:505-512.
27. Richard Zens, F.J. Och and H. Ney. Phrase-based Statistical Machine Translation. In Proceedings of the 39th Annual Meeting of the Association of Computational Lingistics. 2002:35-56.
28. I. Dan Melamed. Multitext Grammars and Synchronous Parsers. In Proceedings of HLT/NAACL, 2003:79-86.
29. Deyi Xiong, Min Zhang, Aiti Aw, Haizhou Li. Linguistically Annotated Reordering: Evaluation and Analysis. Computational Linguistics. 2010,36(3):535-568.
30. Min Zhang and Haizhou LI. Tree Kernel-based SVM with Structured Syntactic Knowledge for BTG-based Phrase Reordering.In Proceedings of EMNLP.2009
31. Jinsong Su, Yang Liu, Haitao Mi, Hongmei Zhao, Yajuan Lüand Qun Liu. 2010. Dependency-Based Bracketing Transduction Grammar for Statistical Machine Translation. Beijing, China. In Proceedings of COLING. 2010: 698-707.
32. Andreas Zollmann, Ashish Venugopal. Syntax Augmented Machine Translation via Chart Parsing. In NAACL - Workshop on statistical machine translation, New York. 2006: 4-9.
33. Yuval Marton and Philip Resnik. Soft Syntactic Constraints for Hierarchical Phrased-Based Translation. The 46th Annual Meeting of the association for Computational Linguistics (ACL). Columbus, Ohio. 2008:16-18.
34. L. Shen, J. Xu, R. Weischedel. A New String-to-dependency Machine Translation Algorithm with a Target Dependency Language Model. Proceedings of ACL-08: HLT. 2008:577–585
35. H. Fox. Phrasal Cohesion and Statistical Machine Translation. Proceedings of theACL-02 conference on Empirical methods in natural language processing. 2002:304–3111
36. Michael Collins, Philipp Koehn, Ivona Kucerova. Clause Restructuring for Statistical Machine Translation. In Proceedings of ACL.2005:531-540.
37. Young-Suk Lee , Bing Zhao , Bing Zhao. Clause Reconstructuring for English-to-Japanese Statistical Machine Translation. In Proceedings of EMNLP. 2010.
38. Karthik Visweswariah, Jiri Navratil, Jeffrey Sorensen, Vijil Chenthamarakshan, Nanda Kambhatla. Syntax Based Reordering with Automatically Derived Rules for Improved Statistical Machine Translation. In Proceedings of COLING. 2010: 1119-1127.
39. Chao Wang, Michael Collins, Philipp Koehn. Chinese Syntactic Reordering for Statistical Machine Translation. In Proceedings of EMNLP.2007:.
40. Franz Josef Och, Ignacio Thayer, Daniel Marcu, Kevin Knight, Dragos Stefan Munteanu, Quamrul Tipu, Michel Galley, and Mark Hopkins. Arabic and Chinese MT at USC/ISI. Presentation given at NIST Machine Translation Evaluation Workshop, 2004.
41. Liang Huang, Kevin Knight, and Aravind Joshi. Statistical Syntax-directed Translation with Extended Domain of Locality. In Proceedings of AMTA, Boston, MA.2006:
42. Yang Liu, Qun Liu, and Shouxun Lin. 2006. Tree-to-String Alignment Template for Statistical Machine Translation. In Proceedings of COLING/ACL, pages 609-616, Sydney, Australia, July
43.刘洋.树到串统计翻译模型研究.中国科学院计算技术研究所博士论文.2007.
44. Michel Galley, Mark Hopkins, Kevin Knight, and Daniel Marcu. 2004. What's in a Translation Rule? In Proceedings of HLT/NAACL 2004, pages 273-280, Boston, USA, May.
45. Michel Galley, Jonathan Graehl, Kevin Knight, Daniel Marcu, Steven DeNeefe, Wei Wang; and Ignacio Thayer. 2006. Scalable Inference and Training of Context-rich Syntactic Translation Models. In Proceedings of COLING/ACL 2006, pages 961-968, Sydney, Australia, July.
46. Daniel Marcu, Wei Wang, Abdessamad Echihabi, and Kevin Knight. 2006. SPMT: Statistical Machine Translation with Syntactified Target Language Phrases. In Proceedings of EMNLP 2006, pages 44-52.
47. Kenji Yamada and Kevin Knight. 2001. A Syntax-based Statistical Translation Model. In Proceedings of ACL 2001, pages 523-530.
48. Brooke Cowan, Ivona Ku?erová, and Michael Collins, 2006. A Discriminative Model for Tree-to-Tree Translation. In Proceedings of EMNLP-2006.
49. Brooke Cowan. 2008. A Tree-to-Tree Model for Statistical Machine Translation. PhD Thesis, Massachusetts Institute of Technology.
50. Yuan Ding, Martha Palmer. 2005. Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars. In Proceedings of ACL 2005, pages 541-548, Ann Arbor, Michigan, June.
51. Jason Eisner. 2003. Learning Non-isomorphic Tree Mappings for Machine Translation. Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (Companion Volume), 205-208, Sapporo, July.
52. Chris Quirk, Arul Menezes, and Colin Cherry. 2005. Dependency treelet translation: Syntactically informed phrasal SMT. In Proceedings of ACL 2005, pages 271-279, Ann Arbor, Michigan, June.
53. Min Zhang, Hongfei Jiang, Aiti Aw, Haizhou Li, Chew Lim Tan, and Sheng Li. 2008. A Tree Sequence Alignment-based Tree-to-Tree Translation Model. In Proceedings of HLT:ACL 2008.
54. S. Shieber, Y. Schabes. 1990. Synchronous Tree-adjoining Grammars. Proceedings of the 13th conference on Computational linguistics-Volume 3. 1990:253–258
55. S. Shieber. 2004. Synchronous Grammars as Tree Transducers. Proceedings of the Seventh International Workshop on Tree Adjoining Grammar and Related Formalisms (TAG+ 7). 2004:88–95
56.蒋宏飞.基于同步树替换文法的统计机器翻译研究.哈尔滨工业大学博士论文.2010.
57. Haitao Mi and Liang Huang. 2008. Forest-based Translation Rule Extraction. In Proc. Of EMNLP.
58.何中军.统计机器翻译中短语及结构的译文选择问题研究.中国科学院计算技术研究所博士论文.2008.
59. Zhaopeng Tu, Yang Liu, Young-Sook Hwang, Qun Liu, and Shouxun Lin. 2010. Dependency Forest for Statistical Machine Translation. In Proceedings of COLING 2010, Beijing, China, August.
60. Deyi Xiong, Qun Liu and Shouxun Lin. 2007. A Dependency Treelet String Correspondence Model for Statistical Machine Translation, In Proceedings of Second Workshop on Statistical Machine Translation, Prague, Czech, June 2007
61.刘鹏远.基于知识自动获取的无指导译文消歧方法研究.哈尔滨工业大学博士论文. 2008.
62. M. Carpuat and D. Wu. 2005. Evaluating the Word Sense Disambiguation Performance of Statistical Machine Translation. In Proceedings of the Second International Joint Conference on Natural Language Processing (IJCNLP), Jeju Island, Republic of Korea, 2005:122–127
63. M. Carpuat and D. Wu. 2005. Word sense disambiguation vs. statistical machine translation.43rd ACL Meeting, Ann Arbor. 2005:387~394
64. David Vickrey, Luke Biewald, Marc Teyssier and Daphne Koller. Word-sense Disambiguation for Machine Translation. In Proceedings of the HLT/EMNLP. Vancouver, BC, 2005:771–778
65. Yee Seng Chan, Hwee Tou Ng and David Chiang. Word Sense Disambiguation Improves Statistical Machine Translation. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague, Czech Republic, June 2007:33–40
66. M. Carpuat and D. Wu. Improving Statistical Machine Translation using Word Sense Disambiguation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, June, 2007:61–72
67.王博.机器翻译系统的自动评价及诊断方法研究.哈尔滨工业大学博士论文. 2009.
68. Christoph Tillmann, Stephen Vogel, Hermman Ney, Hassan Sawaf and Alex Zubiaga. Accelerated DP based Search for Statistical Machine Translation. In Proceedings of ACL. 1998.
69. Mathew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla and JohnMakhoul. A Study of Translation Edit Rate with Targeted Human Annotation. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americans. 2006:223-231.
70. Gregor Leusch, Nicola Ueffing and Hermann Ney. Cder:Efficient MT Evaluation Using Block Movement. In the Proceedings of the Thirteenth Conference of the European Chapter of the Association for Computational Linguistics. 2006: 241-248.
71. K. Papineni,S. Roukos,T. Ward,WJ Zhu. BLEU: a Method for Automatic Evaluation of Machine Translation. 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia. 2002:311-318
72. Automatic Evaluation of Machine Translation Quality Using N-gram Co-occurrence Statistics. See:www.nist.gov/speech/tests/mt/doc/n-gramst- udy.pdf . 2003.
73. Chin-Yew Lin and Franz Josef Och. Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics. In Proceedings of ACL. 2004: 605-618.
74. Joseph P. Turian, Luke Shen, and I. Dan Melamed. 2003. Evaluation of Machine Translation and Its Evaluation. Proceedings of MT Summit. 2003: 386-393.
75. Satanjeev Banerjee and Alon Lavie. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. Proceedings of the ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization. 2005:65-73.
76. J. Albrecht and R. Hwa. Regression for Sentence-Level MT Evaluation with Pseudo References. In Proceedings of ACL. 2007:296.
77. K. Duh. Ranking vs. Regression in Machine Translation Evaluation. In Proceeding of the Third Workshop on Statistical Machine Translation. 2008: 191-194.
78. Maja Popovic and Hermann Ney. Word Error Rate: Decomposition over POS Classes and Application for Error Analysis. In Proceedings of the Second Workshop on Statistical Machine Translation. 2007.
79. Ming Zhou, Bo Wang, Shujie Liu, Mu Li, Dongdong Zhang, Tiejun Zhao. Diagnostic Evaluation of Machine Translation Systems Using AutomaticallyConstructed Linguistic Check-Points. In: Proc. of 22nd Coling. 2008: 1121-1128.
80. Bo Wang, Tie Jun Zhao, Mu Yun Yang, Hong Fei Jiang, and Sheng Li. Stability vs. Effectiveness: Improved Sentence-level Combination of Machine Translation based on Weighted MBR. In: Proceedings of the International Conference on Asian Language Processing. Singapore, Singapore: IEEE. 2009:39-42
81. S. Bangalore, G. Bordel and G. Riccardi. Computing Consensus Translation from Multiple Machine Translation Systems. In Workshop on Automatic Speech Recognition and Understanding, Madonna di Campiglio, Italy. 2001:351-354.
82. Evgeny Matusov, Nicola Ueffing and Hermann Ney. Computing Consensus Translation from Multiple Machine Translation Systems using Enhanced Hypothesis Alignment. In Proceedings of EACL. 2006:33-40.
83. Jinhua Du and Andy Way. Using TERp to Augment the System Combination for SMT. In Proceedings of the Ninth Conference of the Association for Machine Translation AMTA. 2010:.
84. Xiaodong He, Mei Yang, Jianfeng Gao, Patrick Nguyen, and Robert Moore. Indirect-HMM based Hypothesis Alignment for Combining Outputs from Machine Translation Systems. In Proc. of EMNLP. 2008:98-107.
85. Xiaodong He, Kristina Toutanova. Joint Optimization for Machine Translation System Combination. In Proc. of EMNLP. 2009: 1202-1211.
86. Damianos Karakos, Jason Eisner, Sanjeev Khudanpur, and Markus Dreyer. Machine Translation System Combination using ITG-based Alignments. In Proceedings of ACL. 2008: 81-84.
87. Chi-Ho Li, Xiaodong He, Yupeng Liu and Ning Xi. Incremental HMM Alignment for MT System Combination. In Proceedings of ACL. 2009: 949-957.
88. Antti-Veikko I. Rosti, Bing Zhang, Spyros Matsoukas, and Richard Schwartz. Incremental Hypothesis Alignment for Building Confusion Networks with Application to Machine Translation System Combination.In Proceedings of the 3rd ACL Workshop on SMT. 2008: 183-186.
89. Yang Feng, Yang Liu, Haitong Mi, Qun Liu, Yajuan Lu. Lattice-based System Combination for Statistical Machine Translation. In Proceedings of ACL. 2009: 1105-1113.
90. E. Matusov, G. Leusch, R. E. Banchs, N. Bertoldi, D. Dechelotte, M. Paulik, S.Roukos, H. Schwenk and H. Ney. System Combination for Machine Translation of Spoken and Written Language. IEEE Trnasactions on Audio. Speech and Language Processing. 2008: 1222-1237.
91. Yong Zhao and Xiaodong He. Using N-gram based Features for Machine Translation System Combination. NAACL-HLT. 2009.
92. Boxing Chen, Min Zhang, Haizhou Li and Aiti Aw. A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination. In: Proceeding of ACL. 2009: 941-948.
93. Wolfgang Macherey,Franz Josef Och. An Empirical Study on Computing Consensus Translations from Multiple Machine Translation Systems. In Proceedings of ACL. 2007: 986-995.
94. Hongfei Jiang, Muyun Yang, Tiejun Zhao, Sheng Li, Bo Wang. A Statistical Machine Translation Model based on a Synthetic Synchronous Grammar. In Proc. of ACL. 2009: 125-128.
95. Nan Duan, Mu Li, Dongdong Zhang and Ming Zhou. Mixture Model-based Minimum Bayes Risk Decoding using Multiple Machine Translation Systems. COLING. 2010: 313-321.
96. Nan Duan, Mu Li, Tong Xiao and Ming Zhou. The Feature Subspace Method for SMT System Combination. In Proc. of EMNLP 2009:1096-1104.
97. Tong Xiao, Jingbo Zhu, Muhua Zhu and Huizhen Wang. Boosting-based System Combination for Machine Tranlsation. In Proc. of the 48th Annual Meeting of the Association for Computational Linguistics (ACL). Uppsala, Sweden. 2010.
98. Liang Huang and David Chiang. 2005. Better k-best Parsing. In Proceedings of the International Workshop on Parsing Technologies (IWPT), pages 53–64.
99. Liang Huang. Forest reranking: Discriminative parsing with Non-local Features. In Proc. Of ACL/HLT. 2008.
100. Liang Huang and Kenji Sagae. Dynamic Programming for Linear-time Incremental Parsing. In Proceedings of ACL. 2010:1077-1086.
101. Hao Zhang and Daniel Gildea. Efficient Multi-pass Decoding for Synchronous Context Free Grammars. In Proceedings of ACL, Columbus. 2008.
102. Zhifei Li, Jason Eisner and Sanjeev Khudanpur. Variational Decoding for Statistical Machine Translation. In Proceedings of ACL. 2009:593-601.
103. Zhifei Li and Jason Eisner. First- and Second-order Expectation Semirings with Applications to Minimum-Risk Training on Translation Forests. In Proceedings of EMNLP. 2009:40-51.
104. Shankar Kumar, Wolfgang Macherey, Chris Dyer, Franz Och. Ef?cient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices, In Proceedings of ACL. 2009:163-171.
105. Zhifei Li and Sanjeev Khudanpur. Efficient Extraction of Oracle-best Translations from Hypergraphs. In Proceedings of NAACL.2009:9-12.
106. Hao Xiong, Haitao Mi, Yang Liu, and Qun Liu. Forest-based Semantic Role Labeling. In Proceedings of AAAI. 2010.
107. John DeNero, Shankar Kumar, Ciprian Chelba, and Franz Och. Model Combination for Machine Translation. In proceedings of NAACL. 2010: 975-983.
108. Nan Duan, Mu Li, Dongdong Zhang and Ming Zhou. Mixture Model-based Minimum Bayes Risk Decoding using Multiple Machine Translation Systems. In Proc. of COLING. 2010:313-320.
109. Nicola Ueffing, Hermann Ney. Word-Level Confidence Estimation for Machine Translation using Phrase-Based Translation Models. In Proceedings of HLT/EMNLP. 2005:763-770.
110. Nicola Ueffing, Hermann Ney. Word-Level Confidence Estimation for Machine Translation. Computational Linguistics. 2007, 33(1): 9-40.
111. Matthew Snover, Nitin Madnani, Bonnie J.Dorr and Richard Schwartz. Fluency, adequacy, or HTER? Exploring Different Human Judgments with a Tunable MT Metric. In Proceedings of the WMT. 2009:259–268.
112. Necip Fazil Ayan, Jing Zheng and Wen Wang. Improving Alignments for Better Confusion Networks for Combining Machine Translation Systems. In Proceedings of the Coling.2008:33–40.
113. David Kauchak and Regina Barzilay. Paraphrasing for Automatic Evaluation. In Proceedings of the HLT-NAACL. 2006:455–462.
114. Liang Zhou, Chon-Yew Lin and Eduard Hovy. Re-evaluating Machine Translation Results with Paraphrase Support. In Proceedings of the EMNLP. 2006:77–84.
115. Deyi Xiong, Min Zhang, Haizhou Li. Error Detection for Statistical Machine Translation using Linguistic Features. In Proceedings of ACL. 2010:.
116. S Jayaraman, A lavin. Multi-Engine Machine Translation Guided by Explicit Word Matching.In Proc. of ACL.2005:101-104.
117. Khe Chai Sim, William J. Byrne, Mark J.F. Gales, Hichem Sahbi, and Phil C. Woodland.Consensus Network Decoding for Statistical Machine Translation System Combination. In Proc. of ICASSP.2007:105-108.
118. Chi-Ho Li, Dongdong Zhang, Mu Li, Ming Zhou, Chi-Ho Li, Minghui Li,Yi Guan. A Probabilistic Approach to Syntax-based Reordering for Statistical Machine Translation. In Proceedings of ACL. 2007:720.
119. Roger Levy and Christopher Manning. Is It Harder To Parse Chinese, or The Chinese Treebank? Published in Proceedings of ACL. 2003: 439-446.
120. Jianfeng Gao, Mu Li, Andi Wu and Chang-Ning Huang. Chinese Word Segmentation And Named Entity Recognition: A Pragmatic Approach. Computational Linguistics.2005,31(4):531-574.
121. George Foster and Roland Kuhn. Mixture-Model Adaptation for SMT. In Proc. of the Second ACL Workshop on Statistical Machine Translation. 2007:128-136.
122. Wen Wang, Andreas Stolcke, Jing Zheng. Reranking Machine Translation Hypotheses with Structured and Web-based Language Models. In Proc. of IEEE ASRU. 2007:159– 164.
123. Arul Menezes and Chris Quirk. Using Dependency Order Templates to Improve Generality in Translation. In Proc 2nd WMT at ACL, Prague, Czech Republic.2007:1-8.
124. Galen Andrew. A Hybrid Markov/semi-Markov Conditional Random Field for Sequence Segmentation. In Proceedings of EMNLP. Sydney, Australia. 2006: 465-472.
125. Simon Corston-Oliver, Anthony Aue, Kevin Duh, amd Eric Ringger. Multilingual Dependency Parsing using Bayes Point Machines, Proc. of NAACL-HLT, New York. 2006:160-167.
126. Xiaodong He. Using Word-Dependent Transition Models in HMM based Word Alignment for Statistical Machine Translation. In Proc 2nd WMT at ACLPrague, Czech Republic.2007:80-87.
127. Patrick Nguyen, Jianfeng Gao and Milind Mahajan. SRILM: a Scalable Language Modeling Toolkit. Microsoft Research Technical Report MSR-TR-2007-144. 2007.
128. Robert Moore and Chris Quirk. 2007. Faster Beam-Search Decoding for Phrasal Statistical Machine Translation. In Proc. of MT Summit XI.
129. Miller, G. A, Beckwith, R., Fellbaum, C., Gross, D., and Miller, K. Introduction to WordNet: An on-line Lexical Database. In Five Papers on WordNet, CSL report, Cognitive Science Laboratory, Princeton University. International Journal of Lexicography. 1990, 3(4):235~244
130.姚天顺,张俐,高竹. WordNet综述.语言文字应用. 2001, 1:27~32
131. Z. Wu, M. Palmer. Verb Semantics and Lexical Selection. In the Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics. Las Cruces, New Mexico, 1994:133-138.
132. Jia Xu, Richard Zens, Hermann Ney. Sentence Segmentation using IBM Word Alignment Model 1. In: Proceedings of the 10th Annual Conference of the European Association for Machine Translation. Budapest, Hungary: Association for Computational Linguistics, 2005:280-287
133. Jinhua Du and Andy Way. An Incremental Three-pass System Combination Framework by Combining Multiple Hypothesis Alignment Methods. International Journal of Asian Language Processing. 2010,20(1):1--15.
134. Franz Josef Och and H. Ney. A Systematic Comparison of Various Statistical Alignment Models. Computational Linguistics. 2003,29(1):19–51.
135. Antti-Veikko I. Rosti, Spyros Matsoukas, and Richard Schwartz. Improved Word-level System Combination for Machine Translation. In Proceedings of ACL. 2007,45(1):312.
136. Roy Tromble, Shankar Kumar, Franz Och and Wolfgang Macherey. Lattice Minimum Bayes-Risk Decoding for Statistical Machine Translation. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing,. 2008:620-629.
137. Abhishek Arun, Barry Haddow, and Philipp Koehn. A Unified Approach to Minimum Risk Training and Decoding. In Proceedings of 5th Workshop onStatistical Machine Translation. 2010:365-374.
138. Adam Pauls, John DeNero, and Dan Klein. Consensus Training for Consensus Decoding in Machine Translation. In Proceedings of EMNLP. 2009:.
139. John DeNero, David Chiang, and Kevin Knight. Fast Consensus Decoding over Translation Forest. In proceedings of ACL. 2009:567-575.
140. Adam Pauls and Dan Klein. Hierarchical Search for Parsing. In Proceedings of NAACL. 2009:557-565.
141. Chirstopher Dyer, Smaranda Muresan, Philip Resnik. Generalizing Word Lattice Translation. In Proceedings of ACL. 2008.
142. Graeme Blackwood. Lattice rescoring methods for statistical machine translation. Ph.D. Thesis. Cambridge University Engineering Department and Clare College. 2010.
143. Joshua Goodman. Semiring parsing. Computational Linguistics, 25, 1999:573-605.
144. Liang Huang. Advanced Dynamic Programming in Semiring and Hypergraph Frameworks. In COLING, Manchester, UK. 2008:.
145. David Chiang, Yuval Marton, and Philip Resnik. Online large-margin training of syntactic and structural translation features. In Proceedings of the Con ference on Empirical Methods in Natural Language Processing. 2008:224-233.
146. Ng, Andrew. Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proceedings of the Twenty-first International Conference on Machine Learning. 2004:.
147. David A. Smith and Jason Eisner. Minimum Risk Annealing for Training Log-linear Models. In Proceedings of COLING-ACL. 2006:787–794.
148. J. Nocedal. 1980. Updating quasi-Newton Matrices with Limited Storage. Math. Comput. 35 773-782
149. Liang Huang and David Chiang. Forest Rescoring: Faster Decoding with Integrated Language Models. In Proceedings of ACL, Prague, Czech Rep. 2007,45(1):144.
150. Boxing Chen, Min Zhang, Aiti Aw and Haizhou Li. Regenerating Hypotheses for Statistical Machine Translation. In: Proceeding of COLING. Manchester, UK. 2008:05-112
151. Le Zhang. Maximum Entropy Modeling Toolkit for Python and C++. Available at http://homepages.inf.ed.ac.uk/s0450736 /maxent toolkit.html. 2004.
152. A. Berger, S. Della Pietra, and V. Della Pietra. A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics. 1996:22(1):39-71,.
153. A. Stolcke. SRILM: An Extensible Language Modeling Toolkit. In: Proc. of the ICSLP 2002. Colorado, 2002:901-904.
154. R. Kneser, H. Ney. Improved Backing-off for M-gram Language Modeling. In: Proc. of the ICASSP. Detroit, 1995:181-184.
155. Y Zhang, S Vogel, A Waibel. Interpreting BLEU/NIST Scores: How Much Improvement do We Need to Have a Better System. In: Proc. of the LREC .2004:2051-2054.
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