Does Chinese BERT Encode Word Structure?

Yile Wang, Leyang Cui, Yue Zhang


Abstract
Contextualized representations give significantly improved results for a wide range of NLP tasks. Much work has been dedicated to analyzing the features captured by representative models such as BERT. Existing work finds that syntactic, semantic and word sense knowledge are encoded in BERT. However, little work has investigated word features for character languages such as Chinese. We investigate Chinese BERT using both attention weight distribution statistics and probing tasks, finding that (1) word information is captured by BERT; (2) word-level features are mostly in the middle representation layers; (3) downstream tasks make different use of word features in BERT, with POS tagging and chunking relying the most on word features, and natural language inference relying the least on such features.
Anthology ID:
2020.coling-main.254
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2826–2836
Language:
URL:
https://aclanthology.org/2020.coling-main.254
DOI:
10.18653/v1/2020.coling-main.254
Bibkey:
Cite (ACL):
Yile Wang, Leyang Cui, and Yue Zhang. 2020. Does Chinese BERT Encode Word Structure?. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2826–2836, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Does Chinese BERT Encode Word Structure? (Wang et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.254.pdf
Code
 ylwangy/BERT_zh_Analysis
Data
CLUECMNLIWSC