Abstract
Neural language models learn, to varying degrees of accuracy, the grammatical properties of natural languages. In this work, we investigate whether there are systematic sources of variation in the language models’ accuracy. Focusing on subject-verb agreement and reflexive anaphora, we find that certain nouns are systematically understood better than others, an effect which is robust across grammatical tasks and different language models. Surprisingly, we find that across four orders of magnitude, corpus frequency is unrelated to a noun’s performance on grammatical tasks. Finally, we find that a novel noun’s grammatical properties can be few-shot learned from various types of training data. The results present a paradox: there should be less variation in grammatical performance than is actually observed.- Anthology ID:
- 2020.emnlp-main.331
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4040–4054
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.331
- DOI:
- 10.18653/v1/2020.emnlp-main.331
- Cite (ACL):
- Charles Yu, Ryan Sie, Nicolas Tedeschi, and Leon Bergen. 2020. Word Frequency Does Not Predict Grammatical Knowledge in Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4040–4054, Online. Association for Computational Linguistics.
- Cite (Informal):
- Word Frequency Does Not Predict Grammatical Knowledge in Language Models (Yu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.emnlp-main.331.pdf
- Code
- CharlesYu2000/lm-variation
- Data
- WebText