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Aspect based sentiment analysis aims to identify the sentimental tendencytowards a given aspect in text. Fine-tuning of pretrained BERT performsexcellent on this task and achieves state-of-the-art performances. ExistingBERT-based works only utilize the last output layer of BERT and ignore thesemantic knowledge in the intermediate layers. This paper explores thepotential of utilizing BERT intermediate layers to enhance the performance offine-tuning of BERT. To the best of our knowledge, no existing work has beendone on this research. To show the generality, we also apply this approach to anatural language inference task. Experimental results demonstrate theeffectiveness and generality of the proposed approach.

Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference