Abstract
When we build a neural network model predicting the relationship between two sentences, the most general and intuitive approach is to use a Siamese architecture, where the sentence vectors obtained from a shared encoder is given as input to a classifier. For the classifier to work effectively, it is important to extract appropriate features from the two vectors and feed them as input. There exist several previous works that suggest heuristic-based function for matching sentence vectors, however it cannot be said that the heuristics tailored for a specific task generalize to other tasks. In this work, we propose a new matching function, ElBiS, that learns to model element-wise interaction between two vectors. From experiments, we empirically demonstrate that the proposed ElBiS matching function outperforms the concatenation-based or heuristic-based matching functions on natural language inference and paraphrase identification, while maintaining the fused representation compact.- Anthology ID:
- S18-2012
- Volume:
- Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Malvina Nissim, Jonathan Berant, Alessandro Lenci
- Venue:
- *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 107–112
- Language:
- URL:
- https://aclanthology.org/S18-2012
- DOI:
- 10.18653/v1/S18-2012
- Cite (ACL):
- Jihun Choi, Taeuk Kim, and Sang-goo Lee. 2018. Element-wise Bilinear Interaction for Sentence Matching. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 107–112, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Element-wise Bilinear Interaction for Sentence Matching (Choi et al., *SEM 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/S18-2012.pdf
- Data
- SNLI