Cross-Linguistic Syntactic Evaluation of Word Prediction Models

Aaron Mueller, Garrett Nicolai, Panayiota Petrou-Zeniou, Natalia Talmina, Tal Linzen


Abstract
A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatical sentences with high accuracy. However, these studies are based primarily on monolingual evidence from English. To investigate how these models’ ability to learn syntax varies by language, we introduce CLAMS (Cross-Linguistic Assessment of Models on Syntax), a syntactic evaluation suite for monolingual and multilingual models. CLAMS includes subject-verb agreement challenge sets for English, French, German, Hebrew and Russian, generated from grammars we develop. We use CLAMS to evaluate LSTM language models as well as monolingual and multilingual BERT. Across languages, monolingual LSTMs achieved high accuracy on dependencies without attractors, and generally poor accuracy on agreement across object relative clauses. On other constructions, agreement accuracy was generally higher in languages with richer morphology. Multilingual models generally underperformed monolingual models. Multilingual BERT showed high syntactic accuracy on English, but noticeable deficiencies in other languages.
Anthology ID:
2020.acl-main.490
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5523–5539
Language:
URL:
https://aclanthology.org/2020.acl-main.490
DOI:
10.18653/v1/2020.acl-main.490
Bibkey:
Cite (ACL):
Aaron Mueller, Garrett Nicolai, Panayiota Petrou-Zeniou, Natalia Talmina, and Tal Linzen. 2020. Cross-Linguistic Syntactic Evaluation of Word Prediction Models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5523–5539, Online. Association for Computational Linguistics.
Cite (Informal):
Cross-Linguistic Syntactic Evaluation of Word Prediction Models (Mueller et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.490.pdf
Video:
 http://slideslive.com/38929095
Code
 aaronmueller/clams +  additional community code
Data
CLAMS