Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference

Boyuan Pan, Yazheng Yang, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He


Abstract
Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requires to infer the logical relationship between two given sentences. While current approaches mostly focus on the interaction architectures of the sentences, in this paper, we propose to transfer knowledge from some important discourse markers to augment the quality of the NLI model. We observe that people usually use some discourse markers such as “so” or “but” to represent the logical relationship between two sentences. These words potentially have deep connections with the meanings of the sentences, thus can be utilized to help improve the representations of them. Moreover, we use reinforcement learning to optimize a new objective function with a reward defined by the property of the NLI datasets to make full use of the labels information. Experiments show that our method achieves the state-of-the-art performance on several large-scale datasets.
Anthology ID:
P18-1091
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
989–999
Language:
URL:
https://aclanthology.org/P18-1091
DOI:
10.18653/v1/P18-1091
Bibkey:
Cite (ACL):
Boyuan Pan, Yazheng Yang, Zhou Zhao, Yueting Zhuang, Deng Cai, and Xiaofei He. 2018. Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 989–999, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference (Pan et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P18-1091.pdf
Presentation:
 P18-1091.Presentation.pdf
Video:
 https://vimeo.com/285802125
Code
 ZJULearning/DMP
Data
BookCorpusMultiNLISNLI