Paper Info
Title
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Abstract
Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question. While current work on LFQA using large pre-trained model for generation are effective at producing fluent and somewhat relevant content, one primary challenge lies in how to generate a faithful answer that has less hallucinated content. We propose a new end-to-end framework that jointly models answer generation and machine reading. The key idea is to augment the generation model with fine-grained, answer-related salient information which can be viewed as an emphasis on faithful facts. State-of-the-art results on two LFQA datasets, ELI5 and MS MARCO, demonstrate the effectiveness of our method, in comparison with strong baselines on automatic and human evaluation metrics. A detailed analysis further proves the competency of our methods in generating fluent, relevant, and more faithful answers.
Year
DOI
Venue
2022
10.18653/v1/2022.findings-acl.61
FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022)
DocType
Volume
Citations 
Conference
Findings of the Association for Computational Linguistics: ACL 2022
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Dan Su100.34
Xiaoguang Li214119.54
Jindi Zhang300.34
Lifeng Shang448530.96
Xin Jiang515032.43
Qun Liu62149203.11
Pascale Fung767885.84