TY - GEN
T1 - Reading and thinking
T2 - 26th International Conference on Computational Linguistics, COLING 2016
AU - Sha, Lei
AU - Chang, Baobao
AU - Sui, Zhifang
AU - Li, Sujian
N1 - Publisher Copyright:
© 1963-2018 ACL.
PY - 2016
Y1 - 2016
N2 - Recognizing Textual Entailment (RTE) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI1) corpus has made it possible to develop and evaluate deep neural network methods for the RTE task. Previous neural network based methods usually try to encode the two sentences and send them together into multi-layer perceptron, or use LSTM-RNN to link two sentence together while using attention mechanic to enhance the model's ability. In this paper, we propose to use the intensive reading mechanic, which means to re-read the sentence (read the sentence again) according to the memory of the other sentence for a better understanding of the sentence pair. The re-read process can be applied alternatively between the two sentences. Experiments show that we achieve results better than current state-of-art equivalents.
AB - Recognizing Textual Entailment (RTE) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI1) corpus has made it possible to develop and evaluate deep neural network methods for the RTE task. Previous neural network based methods usually try to encode the two sentences and send them together into multi-layer perceptron, or use LSTM-RNN to link two sentence together while using attention mechanic to enhance the model's ability. In this paper, we propose to use the intensive reading mechanic, which means to re-read the sentence (read the sentence again) according to the memory of the other sentence for a better understanding of the sentence pair. The re-read process can be applied alternatively between the two sentences. Experiments show that we achieve results better than current state-of-art equivalents.
UR - https://www.scopus.com/pages/publications/85031900378
M3 - 会议稿件
AN - SCOPUS:85031900378
SN - 9784879747020
T3 - COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers
SP - 2870
EP - 2879
BT - COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016
PB - Association for Computational Linguistics, ACL Anthology
Y2 - 11 December 2016 through 16 December 2016
ER -