Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models

Isabel Papadimitriou, Dan Jurafsky


Abstract
We propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models. We train LSTMs on non-linguistic data and evaluate their performance on natural language to assess which kinds of data induce generalizable structural features that LSTMs can use for natural language. We find that training on non-linguistic data with latent structure (MIDI music or Java code) improves test performance on natural language, despite no overlap in surface form or vocabulary. To pinpoint the kinds of abstract structure that models may be encoding to lead to this improvement, we run similar experiments with two artificial parentheses languages: one which has a hierarchical recursive structure, and a control which has paired tokens but no recursion. Surprisingly, training a model on either of these artificial languages leads the same substantial gains when testing on natural language. Further experiments on transfer between natural languages controlling for vocabulary overlap show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced by pre-training correspond to the cross-linguistic syntactic properties. Our results provide insights into the ways that neural models represent abstract syntactic structure, and also about the kind of structural inductive biases which allow for natural language acquisition.
Anthology ID:
2020.emnlp-main.554
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6829–6839
Language:
URL:
https://aclanthology.org/2020.emnlp-main.554
DOI:
10.18653/v1/2020.emnlp-main.554
Bibkey:
Cite (ACL):
Isabel Papadimitriou and Dan Jurafsky. 2020. Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6829–6839, Online. Association for Computational Linguistics.
Cite (Informal):
Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models (Papadimitriou & Jurafsky, EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.554.pdf
Video:
 https://slideslive.com/38938948
Code
 toizzy/tilt-transfer +  additional community code
Data
MAESTRO