Abstract
Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.- Anthology ID:
- D17-1122
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1179–1189
- Language:
- URL:
- https://aclanthology.org/D17-1122
- DOI:
- 10.18653/v1/D17-1122
- Cite (ACL):
- Gehui Shen, Yunlun Yang, and Zhi-Hong Deng. 2017. Inter-Weighted Alignment Network for Sentence Pair Modeling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1179–1189, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Inter-Weighted Alignment Network for Sentence Pair Modeling (Shen et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D17-1122.pdf
- Data
- WikiQA