Crossing Variational Autoencoders for Answer Retrieval
Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, Meng Jiang
Abstract
Answer retrieval is to find the most aligned answer from a large set of candidates given a question. Learning vector representations of questions/answers is the key factor. Question-answer alignment and question/answer semantics are two important signals for learning the representations. Existing methods learned semantic representations with dual encoders or dual variational auto-encoders. The semantic information was learned from language models or question-to-question (answer-to-answer) generative processes. However, the alignment and semantics were too separate to capture the aligned semantics between question and answer. In this work, we propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions. Experiments show that our method outperforms the state-of-the-art answer retrieval method on SQuAD.- Anthology ID:
- 2020.acl-main.498
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5635–5641
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.498
- DOI:
- 10.18653/v1/2020.acl-main.498
- Cite (ACL):
- Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, and Meng Jiang. 2020. Crossing Variational Autoencoders for Answer Retrieval. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5635–5641, Online. Association for Computational Linguistics.
- Cite (Informal):
- Crossing Variational Autoencoders for Answer Retrieval (Yu et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.498.pdf
- Data
- SQuAD