Data Annealing for Informal Language Understanding Tasks

Jing Gu, Zhou Yu


Abstract
There is a huge performance gap between formal and informal language understanding tasks. The recent pre-trained models that improved formal language understanding tasks did not achieve a comparable result on informal language. We propose data annealing transfer learning procedure to bridge the performance gap on informal natural language understanding tasks. It successfully utilizes a pre-trained model such as BERT in informal language. In the data annealing procedure, the training set contains mainly formal text data at first; then, the proportion of the informal text data is gradually increased during the training process. Our data annealing procedure is model-independent and can be applied to various tasks. We validate its effectiveness in exhaustive experiments. When BERT is implemented with our learning procedure, it outperforms all the state-of-the-art models on the three common informal language tasks.
Anthology ID:
2020.findings-emnlp.282
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:
3153–3159
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.282
DOI:
10.18653/v1/2020.findings-emnlp.282
Bibkey:
Cite (ACL):
Jing Gu and Zhou Yu. 2020. Data Annealing for Informal Language Understanding Tasks. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3153–3159, Online. Association for Computational Linguistics.
Cite (Informal):
Data Annealing for Informal Language Understanding Tasks (Gu & Yu, Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.282.pdf