Modeling Exemplification in Long-form Question Answering via Retrieval

Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, Mohit Iyyer


Abstract
Exemplification is a process by which writers explain or clarify a concept by providing an example. While common in all forms of writing, exemplification is particularly useful in the task of long-form question answering (LFQA), where a complicated answer can be made more understandable through simple examples. In this paper, we provide the first computational study of exemplification in QA, performing a fine-grained annotation of different types of examples (e.g., hypotheticals, anecdotes) in three corpora. We show that not only do state-of-the-art LFQA models struggle to generate relevant examples, but also that standard evaluation metrics such as ROUGE are insufficient to judge exemplification quality. We propose to treat exemplification as a retrieval problem in which a partially-written answer is used to query a large set of human-written examples extracted from a corpus. Our approach allows a reliable ranking-type automatic metrics that correlates well with human evaluation. A human evaluation shows that our model’s retrieved examples are more relevant than examples generated from a state-of-the-art LFQA model.
Anthology ID:
2022.naacl-main.151
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2079–2092
Language:
URL:
https://aclanthology.org/2022.naacl-main.151
DOI:
10.18653/v1/2022.naacl-main.151
Bibkey:
Cite (ACL):
Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, and Mohit Iyyer. 2022. Modeling Exemplification in Long-form Question Answering via Retrieval. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2079–2092, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Modeling Exemplification in Long-form Question Answering via Retrieval (Wang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.151.pdf
Data
ELI5MS MARCONatural Questions