Convolutional Interaction Network for Natural Language Inference

Jingjing Gong, Xipeng Qiu, Xinchi Chen, Dong Liang, Xuanjing Huang


Abstract
Attention-based neural models have achieved great success in natural language inference (NLI). In this paper, we propose the Convolutional Interaction Network (CIN), a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. Specifically, CIN encodes one sentence with the filters dynamically generated based on another sentence. Since the filters may be designed to have various numbers and sizes, CIN can capture more complicated interaction patterns. Experiments on three large datasets demonstrate CIN’s efficacy.
Anthology ID:
D18-1186
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1576–1585
Language:
URL:
https://aclanthology.org/D18-1186
DOI:
10.18653/v1/D18-1186
Bibkey:
Cite (ACL):
Jingjing Gong, Xipeng Qiu, Xinchi Chen, Dong Liang, and Xuanjing Huang. 2018. Convolutional Interaction Network for Natural Language Inference. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1576–1585, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Convolutional Interaction Network for Natural Language Inference (Gong et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1186.pdf
Data
MultiNLISNLI