Difference between revisions of "Question Answering (State of the art)"

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* Ming Tan, Cicero dos Santos, Bing Xiang & Bowen Zhou. 2015. [http://arxiv.org/abs/1511.04108 LSTM-Based Deep Learning Models for Nonfactoid Answer Selection]. In eprint arXiv:1511.04108.
 
* Ming Tan, Cicero dos Santos, Bing Xiang & Bowen Zhou. 2015. [http://arxiv.org/abs/1511.04108 LSTM-Based Deep Learning Models for Nonfactoid Answer Selection]. In eprint arXiv:1511.04108.
 
* 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.
 
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* 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.
 
[[Category:State of the art]]
 
[[Category:State of the art]]

Revision as of 09:36, 24 February 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.


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
Wang et al. (2016) - Lexical Decomposition and Composition Wang et al. (2016) 0.771 0.845

References