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        <title>Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions</title>
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    <abstract>Answering how-to questions remains a major challenge in question answering research. A vast number of narrow, long-tail questions cannot be readily answered using a search engine. Moreover, there is little to no annotated data available to develop such systems. This paper makes a first attempt at generating coherent, long-form answers for how-to questions. We propose new architectures, consisting of passage retrieval, subtopic planning and narrative generation, to consolidate multiple relevant passages into a coherent, explanatory answer. Our subtopic planning module aims to produce a set of relevant, diverse subtopics that serve as the backbone for answer generation to improve topic coherence. We present extensive experiments on a WikiHow dataset repurposed for long-form question answering. Empirical results demonstrate that generating narratives to answer how-to questions is a challenging task. Nevertheless, our architecture incorporated with subtopic planning can produce high-quality, diverse narratives evaluated using automatic metrics and human assessment.</abstract>
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%0 Conference Proceedings
%T Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions
%A Cai, Pengshan
%A Yu, Mo
%A Liu, Fei
%A Yu, Hong
%Y Bosselut, Antoine
%Y Chandu, Khyathi
%Y Dhole, Kaustubh
%Y Gangal, Varun
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Novikova, Jekaterina
%Y Perez-Beltrachini, Laura
%S Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F cai-etal-2022-generating
%X Answering how-to questions remains a major challenge in question answering research. A vast number of narrow, long-tail questions cannot be readily answered using a search engine. Moreover, there is little to no annotated data available to develop such systems. This paper makes a first attempt at generating coherent, long-form answers for how-to questions. We propose new architectures, consisting of passage retrieval, subtopic planning and narrative generation, to consolidate multiple relevant passages into a coherent, explanatory answer. Our subtopic planning module aims to produce a set of relevant, diverse subtopics that serve as the backbone for answer generation to improve topic coherence. We present extensive experiments on a WikiHow dataset repurposed for long-form question answering. Empirical results demonstrate that generating narratives to answer how-to questions is a challenging task. Nevertheless, our architecture incorporated with subtopic planning can produce high-quality, diverse narratives evaluated using automatic metrics and human assessment.
%R 10.18653/v1/2022.gem-1.3
%U https://aclanthology.org/2022.gem-1.3
%U https://doi.org/10.18653/v1/2022.gem-1.3
%P 26-42

Markdown (Informal)

[Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions](https://aclanthology.org/2022.gem-1.3) (Cai et al., GEM 2022)

• Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions (Cai et al., GEM 2022)

ACL

• Pengshan Cai, Mo Yu, Fei Liu, and Hong Yu. 2022. Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions. In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 26–42, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.