论文标题

在生成之前阅读!忠实的长形式问,用机器阅读回答

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

论文作者

Su, Dan, Li, Xiaoguang, Zhang, Jindi, Shang, Lifeng, Jiang, Xin, Liu, Qun, Fung, Pascale

论文摘要

长形的问题回答(LFQA)旨在为给定问题产生段落的答案。尽管使用大型预培训的生成模型在LFQA上进行的当前工作有效地产生流利且有些相关的内容,但主要挑战在于如何产生忠实的答案,而幻觉内容较少。我们提出了一个新的端到端框架,该框架共同建模回答生成和机器阅读。关键的想法是通过使用细粒度,与答案相关的明显信息来增强生成模型,这可以看作是对忠实事实的重点。与强大的基线对自动和人类评估指标相比,两个LFQA数据集ELI5和MS MARCO的最新结果证明了我们方法的有效性。详细的分析进一步证明了我们方法在产生流利,相关和更忠实的答案方面的能力。

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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