Difference between revisions of "Question Answering (State of the art)"
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== Answer Sentence Selection == | == 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. | + | 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. |
| + | |||
| + | * [http://cs.stanford.edu/people/mengqiu/data/qg-emnlp07-data.tgz QA Answer Sentence Selection Dataset]: labeled sentences using TREC QA track data, provided by [http://cs.stanford.edu/people/mengqiu/ Mengqiu Wang] and first used in [http://www.aclweb.org/anthology/D/D07/D07-1003.pdf Wang et al. (2007)]. | ||
| Line 40: | Line 42: | ||
| 0.631 | | 0.631 | ||
| 0.748 | | 0.748 | ||
| + | |- | ||
| + | | S&M (2013) | ||
| + | | Severyn and Moschitti (2013) | ||
| + | | 0.678 | ||
| + | | 0.736 | ||
|- | |- | ||
| Shnarch (2013) - Backward | | Shnarch (2013) - Backward | ||
| Line 51: | Line 58: | ||
| 0.770 | | 0.770 | ||
|- | |- | ||
| + | | Yu (2014) - TRAIN-ALL bigram+count | ||
| + | | Yu et al. (2014) | ||
| + | | 0.711 | ||
| + | | 0.785 | ||
| + | |- | ||
| + | | W&I (2015) | ||
| + | | Wang and Ittycheriah (2015) | ||
| + | | 0.746 | ||
| + | | 0.820 | ||
|} | |} | ||
| + | |||
== References == | == References == | ||
| Line 63: | Line 80: | ||
* Yih, Wen-tau and Chang, Ming-Wei and Meek, Christopher and Pastusiak, Andrzej. 2013. [http://research.microsoft.com/pubs/192357/QA-SentSel-Updated-PostACL.pdf Question Answering Using Enhanced Lexical Semantic Models]. In ACL 2013. | * Yih, Wen-tau and Chang, Ming-Wei and Meek, Christopher and Pastusiak, Andrzej. 2013. [http://research.microsoft.com/pubs/192357/QA-SentSel-Updated-PostACL.pdf Question Answering Using Enhanced Lexical Semantic Models]. In ACL 2013. | ||
* Severyn, Aliaksei and Moschitti, Alessandro. 2013. [http://www.aclweb.org/anthology/D13-1044.pdf Automatic Feature Engineering for Answer Selection and Extraction]. In EMNLP 2013. | * Severyn, Aliaksei and Moschitti, Alessandro. 2013. [http://www.aclweb.org/anthology/D13-1044.pdf Automatic Feature Engineering for Answer Selection and Extraction]. In EMNLP 2013. | ||
| − | + | * Lei Yu, Karl Moritz Hermann, Phil Blunsom, and Stephen Pulman. 2014. [http://arxiv.org/pdf/1412.1632v1.pdf Deep Learning for Answer Sentence Selection]. In NIPS deep learning workshop. | |
| + | * Zhiguo Wang and Abraham Ittycheriah. 2015. [http://arxiv.org/abs/1507.02628 FAQ-based Question Answering via Word Alignment]. In eprint arXiv:1507.02628. | ||
[[Category:State of the art]] | [[Category:State of the art]] | ||
Revision as of 07:24, 10 July 2015
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&I (2015) | Wang and Ittycheriah (2015) | 0.746 | 0.820 |
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.
- Zhiguo Wang and Abraham Ittycheriah. 2015. FAQ-based Question Answering via Word Alignment. In eprint arXiv:1507.02628.