@inproceedings{su-etal-2022-read,
title = "Read before Generate! Faithful Long Form Question Answering with Machine Reading",
author = "Su, Dan and
Li, Xiaoguang and
Zhang, Jindi and
Shang, Lifeng and
Jiang, Xin and
Liu, Qun and
Fung, Pascale",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.61",
doi = "10.18653/v1/2022.findings-acl.61",
pages = "744--756",
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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<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.</abstract>
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%0 Conference Proceedings
%T Read before Generate! Faithful Long Form Question Answering with Machine Reading
%A Su, Dan
%A Li, Xiaoguang
%A Zhang, Jindi
%A Shang, Lifeng
%A Jiang, Xin
%A Liu, Qun
%A Fung, Pascale
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F su-etal-2022-read
%X 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.
%R 10.18653/v1/2022.findings-acl.61
%U https://aclanthology.org/2022.findings-acl.61
%U https://doi.org/10.18653/v1/2022.findings-acl.61
%P 744-756
Markdown (Informal)
[Read before Generate! Faithful Long Form Question Answering with Machine Reading](https://aclanthology.org/2022.findings-acl.61) (Su et al., Findings 2022)
ACL