Abstract
Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations. We present a supervised data mining method using an accurate early fusion model to improve the training of an efficient late fusion retrieval model. We first train an accurate classification model with cross-attention between questions and answers. The cross-attention model is then used to annotate additional passages in order to generate weighted training examples for a neural retrieval model. The resulting retrieval model with additional data significantly outperforms retrieval models directly trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR).- Anthology ID:
- 2021.acl-short.35
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 263–268
- Language:
- URL:
- https://aclanthology.org/2021.acl-short.35
- DOI:
- 10.18653/v1/2021.acl-short.35
- Cite (ACL):
- Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, and Daniel Cer. 2021. Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 263–268, Online. Association for Computational Linguistics.
- Cite (Informal):
- Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation (Yang et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2021.acl-short.35.pdf
- Data
- ReQA, SQuAD