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.- Anthology ID:
- 2022.gem-1.3
- Volume:
- Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- GEM
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 26–42
- Language:
- URL:
- https://aclanthology.org/2022.gem-1.3
- DOI:
- 10.18653/v1/2022.gem-1.3
- Cite (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.
- Cite (Informal):
- Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions (Cai et al., GEM 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.gem-1.3.pdf