Question Answering (State of the art)

Answer Sentence Selection

The task of answer sentence selection is designed for the open-domain question answering setting. Given a question and a set of candidate sentences, the task is to choose the correct sentence that contains the exact answer and can sufficiently support the answer choice.

• QA Answer Sentence Selection Dataset: labeled sentences using TREC QA track data, provided by Mengqiu Wang and first used in Wang et al. (2007).

Algorithm	Reference	MAP	MRR
Punyakanok (2004)	Wang et al. (2007)	0.419	0.494
Cui (2005)	Wang et al. (2007)	0.427	0.526
Wang (2007)	Wang et al. (2007)	0.603	0.685
H&S (2010)	Heilman and Smith (2010)	0.609	0.692
W&M (2010)	Wang and Manning (2010)	0.595	0.695
Yao (2013)	Yao et al. (2013)	0.631	0.748
S&M (2013)	Severyn and Moschitti (2013)	0.678	0.736
Shnarch (2013) - Backward	Shnarch (2013)	0.686	0.754
Yih (2013) - LCLR	Yih et al. (2013)	0.709	0.770
Yu (2014) - TRAIN-ALL bigram+count	Yu et al. (2014)	0.711	0.785
W&N (2015) - Three-Layer BLSTM+BM25	Wang and Nyberg (2015)	0.713	0.791
Feng (2015) - Architecture-II	Tan et al. (2015)	0.711	0.800
S&M (2015)	Severyn and Moschitti (2015)	0.746	0.808
W&I (2015)	Wang and Ittycheriah (2015)	0.746	0.820
Tan (2015) - QA-LSTM/CNN+attention	Tan et al. (2015)	0.728	0.832
dos Santos (2016) - Attentive Pooling CNN	dos Santos et al. (2016)	0.753	0.851

References

• Vasin Punyakanok, Dan Roth, and Wen-Tau Yih. 2004. Mapping dependencies trees: An application to question answering. In Proceedings of the 8th International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, FL, USA.
• Hang Cui, Renxu Sun, Keya Li, Min-Yen Kan, and Tat-Seng Chua. 2005. Question answering passage retrieval using dependency relations. In Proceedings of the 28th ACM-SIGIR International Conference on Research and Development in Information Retrieval, Salvador, Brazil.
• Wang, Mengqiu and Smith, Noah A. and Mitamura, Teruko. 2007. What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA. In EMNLP-CoNLL 2007.
• Heilman, Michael and Smith, Noah A. 2010. Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions. In NAACL-HLT 2010.
• Wang, Mengqiu and Manning, Christopher. 2010. Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering. In COLING 2010.
• E. Shnarch. 2013. Probabilistic Models for Lexical Inference. Ph.D. thesis, Bar Ilan University.
• Yao, Xuchen and Van Durme, Benjamin and Callison-Burch, Chris and Clark, Peter. 2013. Answer Extraction as Sequence Tagging with Tree Edit Distance. In NAACL-HLT 2013.
• Yih, Wen-tau and Chang, Ming-Wei and Meek, Christopher and Pastusiak, Andrzej. 2013. Question Answering Using Enhanced Lexical Semantic Models. In ACL 2013.
• Severyn, Aliaksei and Moschitti, Alessandro. 2013. Automatic Feature Engineering for Answer Selection and Extraction. In EMNLP 2013.
• Lei Yu, Karl Moritz Hermann, Phil Blunsom, and Stephen Pulman. 2014. Deep Learning for Answer Sentence Selection. In NIPS deep learning workshop.
• Di Wang and Eric Nyberg. 2015. A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering. In ACL 2015.
• Minwei Feng, Bing Xiang, Michael R. Glass, Lidan Wang, Bowen Zhou. 2015. Applying deep learning to answer selection: A study and an open task. In ASRU 2015.
• Aliaksei Severyn and Alessandro Moschitti. 2015. Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. In SIGIR 2015.
• Zhiguo Wang and Abraham Ittycheriah. 2015. FAQ-based Question Answering via Word Alignment. In eprint arXiv:1507.02628.
• Ming Tan, Cicero dos Santos, Bing Xiang & Bowen Zhou. 2015. LSTM-Based Deep Learning Models for Nonfactoid Answer Selection. In eprint arXiv:1511.04108.
• Cicero dos Santos, Ming Tan, Bing Xiang & Bowen Zhou. 2015. Attentive Pooling Networks. In eprint arXiv:1602.03609.