Attention Can Reflect Syntactic Structure (If You Let It)

Vinit Ravishankar, Artur Kulmizev, Mostafa Abdou, Anders Søgaard, Joakim Nivre


Abstract
Since the popularization of the Transformer as a general-purpose feature encoder for NLP, many studies have attempted to decode linguistic structure from its novel multi-head attention mechanism. However, much of such work focused almost exclusively on English — a language with rigid word order and a lack of inflectional morphology. In this study, we present decoding experiments for multilingual BERT across 18 languages in order to test the generalizability of the claim that dependency syntax is reflected in attention patterns. We show that full trees can be decoded above baseline accuracy from single attention heads, and that individual relations are often tracked by the same heads across languages. Furthermore, in an attempt to address recent debates about the status of attention as an explanatory mechanism, we experiment with fine-tuning mBERT on a supervised parsing objective while freezing different series of parameters. Interestingly, in steering the objective to learn explicit linguistic structure, we find much of the same structure represented in the resulting attention patterns, with interesting differences with respect to which parameters are frozen.
Anthology ID:
2021.eacl-main.264
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3031–3045
Language:
URL:
https://aclanthology.org/2021.eacl-main.264
DOI:
10.18653/v1/2021.eacl-main.264
Bibkey:
Cite (ACL):
Vinit Ravishankar, Artur Kulmizev, Mostafa Abdou, Anders Søgaard, and Joakim Nivre. 2021. Attention Can Reflect Syntactic Structure (If You Let It). In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3031–3045, Online. Association for Computational Linguistics.
Cite (Informal):
Attention Can Reflect Syntactic Structure (If You Let It) (Ravishankar et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.264.pdf
Data
Universal Dependencies