Abstract
We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. The two models match user queries to FAQ answers and questions, respectively. We alleviate the missing labeled data of the latter by automatically generating high-quality question paraphrases. We show that our model is on par and even outperforms supervised models on existing datasets.- Anthology ID:
- 2020.acl-main.74
- 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:
- 807–812
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.74
- DOI:
- 10.18653/v1/2020.acl-main.74
- Cite (ACL):
- Yosi Mass, Boaz Carmeli, Haggai Roitman, and David Konopnicki. 2020. Unsupervised FAQ Retrieval with Question Generation and BERT. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 807–812, Online. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised FAQ Retrieval with Question Generation and BERT (Mass et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.acl-main.74.pdf