Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications

Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu


Abstract
There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult. Compounding this difficulty is the need to assess varying quality criteria depending on the deployment setting. While the landscape of NLG evaluation has been well-mapped, practitioners’ goals, assumptions, and constraints—which inform decisions about what, when, and how to evaluate—are often partially or implicitly stated, or not stated at all. Combining a formative semi-structured interview study of NLG practitioners (N=18) with a survey study of a broader sample of practitioners (N=61), we surface goals, community practices, assumptions, and constraints that shape NLG evaluations, examining their implications and how they embody ethical considerations.
Anthology ID:
2022.naacl-main.24
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:
314–324
Language:
URL:
https://aclanthology.org/2022.naacl-main.24
DOI:
10.18653/v1/2022.naacl-main.24
Bibkey:
Cite (ACL):
Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, and Alexandra Olteanu. 2022. Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 314–324, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications (Zhou et al., NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.24.pdf