How long is their sentiment analysis dataset?
Three datasets had total of 14.5k samples.
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.
Aspect based sentiment analysis (ABSA) is an important task in natural language processing. It aims at collecting and analyzing the opinions toward the targeted aspect in an entire text. In the past decade, ABSA has received great attention due to a wide range of applications BIBREF0, BIBREF1. Aspect-level (also mentioned as “target-level”) sentiment classification as a subtask of ABSA BIBREF0 aims at judging the sentiment polarity for a given aspect. For example, given a sentence “I hated their service, but their food was great”, the sentiment polarities for the target “service” and “food” are negative and positive respectively. Most of existing methods focus on designing sophisticated deep learning models to mining the relation between context and the targeted aspect. Majumder et al., majumder2018iarm adopt a memory network architecture to incorporate the related information of neighboring aspects. Fan et al., fan2018multi combine the fine-grained and coarse-grained attention to make LSTM treasure the aspect-level interactions. However, the biggest challenge in ABSA task is the shortage of training data, and these complex models did not lead to significant improvements in outcomes. Pre-trained language models can leverage large amounts of unlabeled data to learn the universal language representations, which provide an effective solution for the above problem. Some of the most prominent examples are ELMo BIBREF2, GPT BIBREF3 and BERT BIBREF4. BERT is based on a multi-layer bidirectional Transformer, and is trained on plain text for masked word prediction and next sentence prediction tasks. The pre-trained BERT model can then be fine-tuned on downstream task with task-specific training data. Sun et al., sun2019utilizing utilize BERT for ABSA task by constructing a auxiliary sentences, Xu et al., xu2019bert propose a post-training approach for ABSA task, and Liu et al., liu2019multi combine multi-task learning and pre-trained BERT to improve the performance of various NLP tasks. However, these BERT-based studies follow the canonical way of fine-tuning: append just an additional output layer after BERT structure. This fine-tuning approach ignores the rich semantic knowledge contained in the intermediate layers. Due to the multi-layer structure of BERT, different layers capture different levels of representations for the specific task after fine-tuning. This paper explores the potential of utilizing BERT intermediate layers for facilitating BERT fine-tuning. On the basis of pre-trained BERT, we add an additional pooling module, design some pooling strategies for integrating the multi-layer representations of the classification token. Then, we fine tune the pre-trained BERT model with this additional pooling module and achieve new state-of-the-art results on ABSA task. Additional experiments on a large Natural Language Inference (NLI) task illustrate that our method can be easily applied to more NLP tasks with only a minor adjustment. Main contributions of this paper can be summarized as follows: It is the first to explore the potential of utilizing intermediate layers of BERT and we design two effective information pooling strategies to solve aspect based sentiment analysis task. Experimental results on ABSA datasets show that our method is better than the vanilla BERT model and can boost other BERT-based models with a minor adjustment. Additional experiments on a large NLI dataset illustrate that our method has a certain degree of versatility, and can be easily applied to some other NLP tasks.