Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation
Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, Daniel Cer
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).
BibTeX
@inproceedings{yang-etal-2021-neural-retrieval,
title = "Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation",
author = "Yang, Yinfei and
Jin, Ning and
Lin, Kuo and
Guo, Mandy and
Cer, Daniel",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.35/",
doi = "10.18653/v1/2021.acl-short.35",
pages = "263--268"
}