融合了问句释义和词级别注意力的关系检测模型
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摘要
在知识库问答系统任务中,由于自然语言表达方式的多样性与复杂性,语义相同表达方式不同的问句得到的答案可能不同,生成问句释义可以缓解这一问题。其次,关系检测是知识库问答系统中至关重要的一步,问答系统回答问题的准确性主要受这一步骤的影响,传统的基于注意力机制的关系检测模型没有考虑到答案路径不同抽象级别的不同重要程度。因此,本文提出了基于问句释义和词级别注意力机制的关系检测模型,用于知识库问答系统任务中,实验表明本文模型回答问题准确率较高。
In the knowledge base question answer system, due to the diversity and complexity of natural language expression, the question with the same semantic but different expressions may yield different answer. The generation of paraphrase can alleviate this problem. Secondly, relation detection is a crucial step in the knowledge base question answer system. The accuracy of the question answering system to answer questions is mainly affected by this step. The traditional attention-based relation detection model does not take into account the importance of different part of the different abstract levels of the answer path expression. Therefore, this paper proposes a relation detection model based on paraphrase and word-level attention mechanism, which is used in the knowledge base question answer system end task. Experiments show that the model has higher accuracy in answering questions.
In the knowledge base question answer system, due to the diversity and complexity of natural language expression, the question with the same semantic but different expressions may yield different answer. The generation of paraphrase can alleviate this problem. Secondly, relation detection is a crucial step in the knowledge base question answer system. The accuracy of the question answering system to answer questions is mainly affected by this step. The traditional attention-based relation detection model does not take into account the importance of different part of the different abstract levels of the answer path expression. Therefore, this paper proposes a relation detection model based on paraphrase and word-level attention mechanism, which is used in the knowledge base question answer system end task. Experiments show that the model has higher accuracy in answering questions.
引文
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[20]Zhang Hongzhi,Xu Guangdong,Liang Xiao,et al.An Attention-Based Word-Level Interaction Model:Relation Detection for Knowledge Base Question Answering[J].arXiv preprint arXiv:1801.09893,2018.
[2]Fabian M.Suchanek,Gjergji Kasneci,Gerhard Weikum.Yago:a core of semantic knowledge[C]//International Conference on World Wide Web.2007,pp.697-706.
[3]Sren Auer,Christian Bizer,Georgi Kobilarov,Jens Lehmann,Richard Cyganiak,Zachary.DBpedia:A Nucleus for a Web of Open Data[C]//Semantic Web,International Semantic Web Conference,Asian Semantic Web Conference,Iswc+Aswc,Busan,Korea,November.2007.
[4]Jonathan Berant,Andrew Chou,Roy Frostig,and Percy Liang.2013.Semantic parsing on freebase from questionanswer pairs[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.2013:1533-1544.
[5]Wen-tau Yih,Xiaodong He,and Christopher Meek.Semantic parsing for single-relation question answering[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics(Volume 2:Short Papers).2014,2:643-648.
[6]Xuchen Yao and Benjamin Van Durme.Information extraction over structured data:Question answering with freebase[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2014,1:956-966.
[7]Kun Xu,Yansong Feng,Songfang Huang,and Dongyan Zhao.Hybrid question answering over knowledge base and free text[C]//Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics:Technical Papers.2016:2397-2407.
[8]Antoine Bordes,Sumit Chopra,and Jason Weston.2014a.Question answering with sub-graph embeddings[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP).Association for Computational Linguistics,pages 615-620.
[9]Daojian Zeng,Kang Liu,Siwei Lai,Guangyou Zhou,and Jun Zhao.2014.Relation classification via convolutional deep neural network[J].In Proceedings of COLING 2014,the 25th International Conference on Computational Linguistics:Technical Papers.Dublin City University and Association for Computational Linguistics,Dublin,Ireland,pages 2335-2344.
[10]Li Dong,Furu Wei,Ming Zhou,and Ke Xu.Question answering over freebase with multi-column convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Volume 1:Long Papers).2015,1:260-269.
[11]Linlin Wang,Zhu Cao,Gerard de Melo,and Zhiyuan Liu.Relation classification via multi-level attention cnns[J]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).Association for Computational Linguistics,Berlin,Germany.2016,pages 1298-1307.
[12]Antoine Bordes,Nicolas Usunier,Alberto Garcia-Duran.Translating embeddings for modeling multi-relational data[C]//Advances in neural information processing systems.2013:2787-2795.
[13]Wenpeng Yin,Mo Yu,Bing Xiang,Bowen Zhou,Hinrich Schütze,Simple question answering by attentive convolutional neural network[J].arXiv preprint arXiv:1606.03391,2016.
[14]Mo Yu,Wenpeng Yin,Kazi Saidul Hasan,Cicero dos Santos,Bing Xiang,Bowen Zhou.Improved Neural Relation Detection for Knowledge Base Question Answering[J].In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers),Association for Computational Linguistics,Vancouver,Canada,2017,pp.571-581.
[15]Antoine Bordes,Jason Weston,Nicolas Usunier.Open Question Answering with Weakly Supervised Embedding Models[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Springer-Verlag New York,Inc.2014.
[16]Shashi Narayan,Siva Reddy,and Shay B Cohen.Paraphrase generation from Latent-Variable PCFGs for semantic parsing[J].arXiv preprint arXiv:1601.06068,2016.
[17]Bo Chen,Le Sun,Xianpei Han,and Bo An.Sentence rewriting for semantic parsing[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers),2016,pages 766-777.
[18]Ellie Pavlick,Pushpendre Rastogi,Juri Ganitkevitch,Benjamin Van Durme,and Chris Callison-Burch.PPDB 2.0:Better paraphrase ranking,fine-grained entailment relations,word embeddings,and style classification[C]//Proceedings of the53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Volume 2:Short Papers).2015,2:425-430.
[19]Dzmitry Bahdanau,Kyunghyun Cho,and Yoshua Bengio.Neural machine translation by jointly learning to align and translate[J].arXiv preprint arXiv:1409.0473,2014.
[20]Zhang Hongzhi,Xu Guangdong,Liang Xiao,et al.An Attention-Based Word-Level Interaction Model:Relation Detection for Knowledge Base Question Answering[J].arXiv preprint arXiv:1801.09893,2018.