论文标题
利用BERT中间层进行基于方面的情感分析和自然语言推断
Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference
论文作者
论文摘要
基于方面的情感分析旨在确定对文本中给定方面的情感趋势。预审计的BERT的微调在这项任务上表现出色，并实现了最先进的表演。现有的基于BERT的作品仅利用BERT的最后输出层，而忽略了中间层中的语义知识。本文探讨了利用BERT中间层来增强BERT微调的性能的潜力。据我们所知，在这项研究上没有进行现有的工作。为了显示一般性，我们还将这种方法应用于自然语言推理任务。实验结果证明了拟议方法的有效性和普遍性。
Aspect based sentiment analysis aims to identify the sentimental tendency towards a given aspect in text. Fine-tuning of pretrained BERT performs excellent on this task and achieves state-of-the-art performances. Existing BERT-based works only utilize the last output layer of BERT and ignore the semantic knowledge in the intermediate layers. This paper explores the potential of utilizing BERT intermediate layers to enhance the performance of fine-tuning of BERT. To the best of our knowledge, no existing work has been done on this research. To show the generality, we also apply this approach to a natural language inference task. Experimental results demonstrate the effectiveness and generality of the proposed approach.
