Investigating representations of verb bias in neural language models

Robert Hawkins, Takateru Yamakoshi, Thomas Griffiths, Adele Goldberg


Abstract
Languages typically provide more than one grammatical construction to express certain types of messages. A speaker’s choice of construction is known to depend on multiple factors, including the choice of main verb – a phenomenon known as verb bias. Here we introduce DAIS, a large benchmark dataset containing 50K human judgments for 5K distinct sentence pairs in the English dative alternation. This dataset includes 200 unique verbs and systematically varies the definiteness and length of arguments. We use this dataset, as well as an existing corpus of naturally occurring data, to evaluate how well recent neural language models capture human preferences. Results show that larger models perform better than smaller models, and transformer architectures (e.g. GPT-2) tend to out-perform recurrent architectures (e.g. LSTMs) even under comparable parameter and training settings. Additional analyses of internal feature representations suggest that transformers may better integrate specific lexical information with grammatical constructions.
Anthology ID:
2020.emnlp-main.376
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:
4653–4663
Language:
URL:
https://aclanthology.org/2020.emnlp-main.376
DOI:
10.18653/v1/2020.emnlp-main.376
Bibkey:
Cite (ACL):
Robert Hawkins, Takateru Yamakoshi, Thomas Griffiths, and Adele Goldberg. 2020. Investigating representations of verb bias in neural language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4653–4663, Online. Association for Computational Linguistics.
Cite (Informal):
Investigating representations of verb bias in neural language models (Hawkins et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.376.pdf
Video:
 https://slideslive.com/38939281
Code
 taka-yamakoshi/neural_constructions
Data
DAIS