Research Area:  Machine Learning
Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets.
Author(s) Name:  Chi Sun,Xipeng Qiu, Yige Xu,Xuanjing Huang
Conferrence name:  China National Conference on Chinese Computational Linguistics
Publisher name:  SPRINGER
Paper Link:   https://link.springer.com/chapter/10.1007/978-3-030-32381-3_16