Read before Generate! Faithful Long Form Question Answering with Machine Reading

• URL: http://arxiv.org/abs/2203.00343v1
• Date: Tue, 1 Mar 2022 10:41:17 GMT
• Title: Read before Generate! Faithful Long Form Question Answering with Machine Reading
• Authors: Dan Su, Xiaoguang Li, Jindi Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Pascale Fung
• Abstract summary: Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question. We propose a new end-to-end framework that jointly models answer generation and machine reading.
• Score: 77.17898499652306
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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