论文标题
生成长形的问题回答：相关性，忠诚和简洁
Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness
论文作者
论文摘要
在这篇论文中，我们调查了长期问答（LFQA）的相关性，忠诚和简洁方面。 LFQA的目的是为给定问题生成一个深入的段落长度答案，以帮助弥合实际场景和现有的开放式质量主QA模型之间的差距，该模型只能提取短跨度的答案。 LFQA非常具有挑战性且探索不足。几乎没有做过有效的LFQA系统的工作。产生与查询和忠实于事实相关的优质的长格式答案更具挑战性，因为在检索的文档中将包含大量的冗余，补充或矛盾的信息。此外，尚未对先前的工作进行简洁的答案。我们是第一个研究LFQA任务的人之一。我们开创了研究方向，以提高回答质量的方向为1）查询 - 相关，2）答案忠诚和3）回答简洁。
In this thesis, we investigated the relevance, faithfulness, and succinctness aspects of Long Form Question Answering (LFQA). LFQA aims to generate an in-depth, paragraph-length answer for a given question, to help bridge the gap between real scenarios and the existing open-domain QA models which can only extract short-span answers. LFQA is quite challenging and under-explored. Few works have been done to build an effective LFQA system. It is even more challenging to generate a good-quality long-form answer relevant to the query and faithful to facts, since a considerable amount of redundant, complementary, or contradictory information will be contained in the retrieved documents. Moreover, no prior work has been investigated to generate succinct answers. We are among the first to research the LFQA task. We pioneered the research direction to improve the answer quality in terms of 1) query-relevance, 2) answer faithfulness, and 3) answer succinctness.
