[2211.11304] TCBERT: A Technical Report for Chinese Topic Classification BERT
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Computer Science > Computation and Language

arXiv:2211.11304 (cs)
[Submitted on 21 Nov 2022]

Title:TCBERT: A Technical Report for Chinese Topic Classification BERT

Authors:Ting Han, Kunhao Pan, Xinyu Chen, Dingjie Song, Yuchen Fan, Xinyu Gao, Ruyi Gan, Jiaxing Zhang
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Abstract:Bidirectional Encoder Representations from Transformers or BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various NLP tasks due to its remarkable performance. Variants customized for different languages and tasks are proposed to further improve the performance. In this work, we investigate supervised continued pre-training~\cite{gururangan-etal-2020-dont} on BERT for Chinese topic classification task. Specifically, we incorporate prompt-based learning and contrastive learning into the pre-training. To adapt to the task of Chinese topic classification, we collect around 2.1M Chinese data spanning various topics. The pre-trained Chinese Topic Classification BERTs (TCBERTs) with different parameter sizes are open-sourced at \url{this https URL}.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2211.11304 [cs.CL]
  (or arXiv:2211.11304v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2211.11304

Submission history

From: Ting Han [view email]
[v1] Mon, 21 Nov 2022 09:45:15 UTC (7,171 KB)
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