VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding

Dou Hu, Xiaolong Hou, Xiyang Du, Mengyuan Zhou, Lianxin Jiang, Yang Mo, Xiaofeng Shi


Abstract
Pre-trained language models have been widely applied to standard benchmarks. Due to the flexibility of natural language, the available resources in a certain domain can be restricted to support obtaining precise representation. To address this issue, we propose a novel Transformer-based language model named VarMAE for domain-adaptive language understanding. Under the masked autoencoding objective, we design a context uncertainty learning module to encode the token’s context into a smooth latent distribution. The module can produce diverse and well-formed contextual representations. Experiments on science- and finance-domain NLU tasks demonstrate that VarMAE can be efficiently adapted to new domains with limited resources.
Anthology ID:
2022.findings-emnlp.468
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
Findings
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Publisher:
Association for Computational Linguistics
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Pages:
6276–6286
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.468
DOI:
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Cite (ACL):
Dou Hu, Xiaolong Hou, Xiyang Du, Mengyuan Zhou, Lianxin Jiang, Yang Mo, and Xiaofeng Shi. 2022. VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6276–6286, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding (Hu et al., Findings 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.findings-emnlp.468.pdf