论文标题
越南评论情感分析的微调伯特
Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews
论文作者
论文摘要
情感分析是核语言处理（NLP）领域的重要任务，其中评估和分析了用户对特定问题的反馈。已经提出了Many-Deep学习模型来应对这项任务，包括来自变形金刚（BERT）模型的最近引入的双向编码器启动。在本文中，我们在越南评论的数据集上使用两种BERT微调方法进行索取分析任务：1）仅使用[Cls]令牌作为厌食的馈送神经网络的输入，以及2）所有BERT输出量都用作输入的方法，作为分类量的输入。两个数据集上的实验结果表明，使用BERT使用glove和fastText的其他模型模型模型。此外，关于这项研究的数据集，我们提出的BERT微调方法比原始的BERT微调方法产生的AMODEL具有更好的性能。
Sentiment analysis is an important task in the field ofNature Language Processing (NLP), in which users' feedbackdata on a specific issue are evaluated and analyzed. Manydeep learning models have been proposed to tackle this task, including the recently-introduced Bidirectional Encoder Rep-resentations from Transformers (BERT) model. In this paper,we experiment with two BERT fine-tuning methods for thesentiment analysis task on datasets of Vietnamese reviews: 1) a method that uses only the [CLS] token as the input for anattached feed-forward neural network, and 2) another methodin which all BERT output vectors are used as the input forclassification. Experimental results on two datasets show thatmodels using BERT slightly outperform other models usingGloVe and FastText. Also, regarding the datasets employed inthis study, our proposed BERT fine-tuning method produces amodel with better performance than the original BERT fine-tuning method.
