LIMIT-BERT : Linguistics Informed Multi-Task BERT

Junru Zhou, Zhuosheng Zhang, Hai Zhao, Shuailiang Zhang

Abstract
In this paper, we present Linguistics Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistics tasks by Multi-Task Learning. LIMIT-BERT includes five key linguistics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Different from recent Multi-Task Deep Neural Networks (MT-DNN), our LIMIT-BERT is fully linguistics motivated and thus is capable of adopting an improved masked training objective according to syntactic and semantic constituents. Besides, LIMIT-BERT takes a semi-supervised learning strategy to offer the same large amount of linguistics task data as that for the language model training. As a result, LIMIT-BERT not only improves linguistics tasks performance but also benefits from a regularization effect and linguistics information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT outperforms the strong baseline Whole Word Masking BERT on both dependency and constituent syntactic/semantic parsing, GLUE benchmark, and SNLI task. Our practice on the proposed LIMIT-BERT also enables us to release a well pre-trained model for multi-purpose of natural language processing tasks once for all.
Anthology ID:
2020.findings-emnlp.399
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4450–4461
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.399
DOI:
10.18653/v1/2020.findings-emnlp.399
Bibkey:
Cite (ACL):
Junru Zhou, Zhuosheng Zhang, Hai Zhao, and Shuailiang Zhang. 2020. LIMIT-BERT : Linguistics Informed Multi-Task BERT. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4450–4461, Online. Association for Computational Linguistics.
Cite (Informal):
LIMIT-BERT : Linguistics Informed Multi-Task BERT (Zhou et al., Findings 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.399.pdf
Optional supplementary material:
 2020.findings-emnlp.399.OptionalSupplementaryMaterial.zip
Code
 DoodleJZ/LIMIT-BERT
Data
GLUE, Penn Treebank, SNLI
PDF Search Code Optional supplementary material