Learning Semantic Textual Similarity from Conversations
Yinfei Yang, Steve Yuan, Daniel Cer, Sheng-yi Kong, Noah Constant, Petr Pilar, Heming Ge, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
Abstract
We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational responses. The resulting sentence embeddings perform well on the Semantic Textual Similarity (STS) Benchmark and SemEval 2017’s Community Question Answering (CQA) question similarity subtask. Performance is further improved by introducing multitask training, combining conversational response prediction and natural language inference. Extensive experiments show the proposed model achieves the best performance among all neural models on the STS Benchmark and is competitive with the state-of-the-art feature engineered and mixed systems for both tasks.- Anthology ID:
- W18-3022
- Volume:
- Proceedings of the Third Workshop on Representation Learning for NLP
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Isabelle Augenstein, Kris Cao, He He, Felix Hill, Spandana Gella, Jamie Kiros, Hongyuan Mei, Dipendra Misra
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 164–174
- Language:
- URL:
- https://aclanthology.org/W18-3022
- DOI:
- 10.18653/v1/W18-3022
- Cite (ACL):
- Yinfei Yang, Steve Yuan, Daniel Cer, Sheng-yi Kong, Noah Constant, Petr Pilar, Heming Ge, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018. Learning Semantic Textual Similarity from Conversations. In Proceedings of the Third Workshop on Representation Learning for NLP, pages 164–174, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Learning Semantic Textual Similarity from Conversations (Yang et al., RepL4NLP 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W18-3022.pdf
- Data
- SNLI