Difference between revisions of "Question Answering (State of the art)"
Jump to navigation
Jump to search
Zhiguo Wang (talk | contribs) |
Raojinfeng (talk | contribs) |
||
| Line 3: | Line 3: | ||
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)]. | + | * [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)]. Over time, this dataset diverged to two versions: both have the same training set but their development and test sets differ due to different pre-processing. The raw version has 82 questions in the development set and 100 questions in the test set. Recently, the raw dataset was cleaned by some researchers (Tan et al. 2015, dos Santos et al. 2016, Wang et al. 2016) by removing those questions with no answers or with only positive/negative answers, leaving 65 questions in the development set and 68 questions in the test set. |
{| border="1" cellpadding="5" cellspacing="1" | {| border="1" cellpadding="5" cellspacing="1" | ||
|- | |- | ||
| − | ! Algorithm | + | ! Algorithm -- Raw Version |
! Reference | ! Reference | ||
! [http://en.wikipedia.org/wiki/Mean_average_precision MAP] | ! [http://en.wikipedia.org/wiki/Mean_average_precision MAP] | ||
| Line 82: | Line 82: | ||
| 0.746 | | 0.746 | ||
| 0.820 | | 0.820 | ||
| + | |- | ||
| + | | H&L (2015) | ||
| + | | He and Lin (2016) | ||
| + | | 0.755 | ||
| + | | 0.825 | ||
| + | |- | ||
| + | | Rao (2016) - Pairwise + Multiple Perspective CNN | ||
| + | | Rao et al. (2016) | ||
| + | | 0.780 | ||
| + | | 0.834 | ||
| + | |} | ||
| + | |||
| + | |||
| + | {| border="1" cellpadding="5" cellspacing="1" | ||
| + | |- | ||
| + | ! Algorithm -- Clean Version | ||
| + | ! Reference | ||
| + | ! [http://en.wikipedia.org/wiki/Mean_average_precision MAP] | ||
| + | ! [http://en.wikipedia.org/wiki/Mean_reciprocal_rank MRR] | ||
|- | |- | ||
| Tan (2015) - QA-LSTM/CNN+attention | | Tan (2015) - QA-LSTM/CNN+attention | ||
| Line 97: | Line 116: | ||
| 0.771 | | 0.771 | ||
| 0.845 | | 0.845 | ||
| + | |- | ||
| + | | Rao et al. (2016) - Pairwise + Multiple Perspective CNN | ||
| + | | Rao et al. (2016) | ||
| + | | 0.801 | ||
| + | | 0.877 | ||
|} | |} | ||
| Line 117: | Line 141: | ||
* Cicero dos Santos, Ming Tan, Bing Xiang & Bowen Zhou. 2016. [http://arxiv.org/abs/1602.03609 Attentive Pooling Networks]. In eprint arXiv:1602.03609. | * Cicero dos Santos, Ming Tan, Bing Xiang & Bowen Zhou. 2016. [http://arxiv.org/abs/1602.03609 Attentive Pooling Networks]. In eprint arXiv:1602.03609. | ||
* Zhiguo Wang, Haitao Mi and Abraham Ittycheriah. 2016. [http://arxiv.org/pdf/1602.07019v1.pdf Sentence Similarity Learning by Lexical Decomposition and Composition]. In eprint arXiv:1602.07019. | * Zhiguo Wang, Haitao Mi and Abraham Ittycheriah. 2016. [http://arxiv.org/pdf/1602.07019v1.pdf Sentence Similarity Learning by Lexical Decomposition and Composition]. In eprint arXiv:1602.07019. | ||
| + | * Hua He and Jimmy Lin. 2016. [https://cs.uwaterloo.ca/~jimmylin/publications/He_etal_NAACL-HTL2016.pdf Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement]. In NAACL 2016. | ||
| + | * Jinfeng Rao, Hua He and Jimmy Lin. 2016. [http://www.cs.umd.edu/~jinfeng/publications/PairwiseNeuralNetwork_CIKM2016.pdf Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks]. In CIKM 2016 | ||
[[Category:State of the art]] | [[Category:State of the art]] | ||
Revision as of 10:17, 20 October 2016
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). Over time, this dataset diverged to two versions: both have the same training set but their development and test sets differ due to different pre-processing. The raw version has 82 questions in the development set and 100 questions in the test set. Recently, the raw dataset was cleaned by some researchers (Tan et al. 2015, dos Santos et al. 2016, Wang et al. 2016) by removing those questions with no answers or with only positive/negative answers, leaving 65 questions in the development set and 68 questions in the test set.
| Algorithm -- Raw Version | 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 |
| H&L (2015) | He and Lin (2016) | 0.755 | 0.825 |
| Rao (2016) - Pairwise + Multiple Perspective CNN | Rao et al. (2016) | 0.780 | 0.834 |
| Algorithm -- Clean Version | Reference | MAP | MRR |
|---|---|---|---|
| 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 |
| Wang et al. (2016) - Lexical Decomposition and Composition | Wang et al. (2016) | 0.771 | 0.845 |
| Rao et al. (2016) - Pairwise + Multiple Perspective CNN | Rao et al. (2016) | 0.801 | 0.877 |
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. 2016. Attentive Pooling Networks. In eprint arXiv:1602.03609.
- Zhiguo Wang, Haitao Mi and Abraham Ittycheriah. 2016. Sentence Similarity Learning by Lexical Decomposition and Composition. In eprint arXiv:1602.07019.
- Hua He and Jimmy Lin. 2016. Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement. In NAACL 2016.
- Jinfeng Rao, Hua He and Jimmy Lin. 2016. Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks. In CIKM 2016