Refining Targeted Syntactic Evaluation of Language Models

Benjamin Newman, Kai-Siang Ang, Julia Gong, John Hewitt


Abstract
Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models’ syntactic knowledge using hand-crafted minimal pairs of sentences that differ only in the main verb’s conjugation. The method evaluates whether language models rate each grammatical sentence as more likely than its ungrammatical counterpart. We identify two distinct goals for TSE. First, evaluating the systematicity of a language model’s syntactic knowledge: given a sentence, can it conjugate arbitrary verbs correctly? Second, evaluating a model’s likely behavior: given a sentence, does the model concentrate its probability mass on correctly conjugated verbs, even if only on a subset of the possible verbs? We argue that current implementations of TSE do not directly capture either of these goals, and propose new metrics to capture each goal separately. Under our metrics, we find that TSE overestimates systematicity of language models, but that models score up to 40% better on verbs that they predict are likely in context.
Anthology ID:
2021.naacl-main.290
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3710–3723
Language:
URL:
https://aclanthology.org/2021.naacl-main.290
DOI:
10.18653/v1/2021.naacl-main.290
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.290.pdf