BERT微调策略探索：文本分类新高度

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"文本分类微调Bert1" 在自然语言处理（NLP）领域，文本分类是一项基础但至关重要的任务。它的目标是为给定的文本序列分配预定义的类别。随着深度学习技术的发展，预训练语言模型，尤其是BERT（Bidirectional Encoder Representations from Transformers），已经成为提升文本理解能力的前沿工具。BERT通过Transformer架构实现了双向上下文建模，已在多项语言理解任务上取得了卓越的效果。本文聚焦于BERT在文本分类任务上的微调策略，进行了详尽的实验研究，以期提供一个通用的BERT微调解决方案。在实验中，作者Chi Sun、Xipeng Qiu、Yige Xu和Xuanjing Huang分别来自复旦大学的智能信息处理国家重点实验室和计算机科学学院，他们的工作地点位于中国上海。传统的文本分类方法通常依赖于手工特征工程，而BERT等预训练模型则通过大量未标注文本自动生成通用的语言表示，降低了对特定任务特征工程的依赖。在微调阶段，这些模型可以针对下游任务进行调整，例如文本分类，以提高性能。论文首先介绍了不同的BERT微调方法，可能包括对全模型、部分层或者只对分类头（即最后一层的线性分类器）进行微调。通过对比实验，作者们分析了每种方法的优点和缺点，以及它们在不同数据集上的表现。在实验部分，论文选择了八个广泛研究的文本分类数据集，这些数据集涵盖了各种主题和类别，例如情感分析、新闻分类等。通过对比多种微调策略，作者们提出了一种优化的微调方案，该方案在这些数据集上达到了新的最优结果。此外，论文还可能讨论了学习率调度、批量大小、训练轮数等超参数的影响，以及如何有效地利用预训练模型的权重初始化来加速收敛和提高性能。最后，作者可能总结了BERT微调的最佳实践，并对未来的研究方向提出了建议，如更高效的学习策略、模型压缩和适应低资源环境的微调方法。这篇论文对BERT在文本分类任务中的微调进行了深入研究，不仅提供了实际操作的指导，也推动了NLP领域对预训练模型应用的理解和优化。对于想要利用BERT进行文本分类的开发者和研究人员来说，这是一份极具价值的参考资料。

How to Fine-Tune BERT for Text Classiﬁcation?

Chi Sun, Xipeng Qiu

∗

, Yige Xu, Xuanjing Huang

Shanghai Key Laboratory of Intelligent Information Processing, Fudan University

School of Computer Science, Fudan University

825 Zhangheng Road, Shanghai, China

{sunc17,xpqiu,ygxu18,xjhuang}@fudan.edu.cn

Abstract

Language model pre-training has proven to be

useful in learning universal language represen-

tations. As a state-of-the-art language model

pre-training model, BERT (Bidirectional En-

coder Representations from Transformers) has

achieved amazing results in many language

understanding tasks. In this paper, we con-

duct exhaustive experiments to investigate dif-

ferent ﬁne-tuning methods of BERT on text

classiﬁcation task and provide a general solu-

tion for BERT ﬁne-tuning. Finally, the pro-

posed solution obtains new state-of-the-art re-

sults on eight widely-studied text classiﬁcation

datasets.

1 Introduction

Text classiﬁcation is a classic problem in Natural

Language Processing (NLP). The task is to assign

predeﬁned categories to a given text sequence. An

important intermediate step is the text representa-

tion. Previous work uses various neural models

to learn text representation, including convolution

models (Kalchbrenner et al., 2014; Zhang et al.,

2015; Conneau et al., 2016; Johnson and Zhang,

2017; Zhang et al., 2017; Shen et al., 2018), re-

current models (Liu et al., 2016; Yogatama et al.,

2017; Seo et al., 2017), and attention mechanisms

(Yang et al., 2016; Lin et al., 2017).

Alternatively, substantial work has shown that

pre-trained models on large corpus are beneﬁcial

for text classiﬁcation and other NLP tasks, which

can avoid training a new model from scratch. One

kind of pre-trained models is the word embed-

dings, such as word2vec (Mikolov et al., 2013)

and GloVe (Pennington et al., 2014), or the con-

textualized word embeddings, such as CoVe (Mc-

Cann et al., 2017) and ELMo (Peters et al.,

∗

Corresponding author

The source codes are available at https://github.

com/xuyige/BERT4doc-Classification.

2018). These word embeddings are often used

as additional features for the main task. An-

other kind of pre-training models is sentence-

level. Howard and Ruder (2018) propose ULM-

FiT, a ﬁne-tuning method for pre-trained language

model that achieves state-of-the-art results on six

widely studied text classiﬁcation datasets. More

recently, pre-trained language models have shown

to be useful in learning common language rep-

resentations by utilizing a large amount of unla-

beled data: e.g., OpenAI GPT (Radford et al.,

2018) and BERT (Devlin et al., 2018). BERT is

based on a multi-layer bidirectional Transformer

(Vaswani et al., 2017) and is trained on plain text

for masked word prediction and next sentence pre-

diction tasks.

Although BERT has achieved amazing results

in many natural language understanding (NLU)

tasks, its potential has yet to be fully explored.

There is little research to enhance BERT to im-

prove the performance on target tasks further.

In this paper, we investigate how to maximize

the utilization of BERT for the text classiﬁca-

tion task. We explore several ways of ﬁne-tuning

BERT to enhance its performance on text classiﬁ-

cation task. We design exhaustive experiments to

make a detailed analysis of BERT.

The contributions of our paper are as follows:

• We propose a general solution to ﬁne-tune

the pre-trained BERT model, which includes

three steps: (1) further pre-train BERT on

within-task training data or in-domain data;

(2) optional ﬁne-tuning BERT with multi-

task learning if several related tasks are avail-

able; (3) ﬁne-tune BERT for the target task.

• We also investigate the ﬁne-tuning meth-

ods for BERT on target task, including pre-

process of long text, layer selection, layer-

wise learning rate, catastrophic forgetting,

arXiv:1905.05583v3 [cs.CL] 5 Feb 2020

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