Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning

by   Jinsong Su, et al.

In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. However, such a mechanism suffers from one drawback: only a few frequent words with sentiment polarities are tended to be taken into consideration for final sentiment decision while abundant infrequent sentiment words are ignored by models. To deal with this issue, we propose a progressive self-supervised attention learning approach for attentional ABSA models. In this approach, we iteratively perform sentiment prediction on all training instances, and continually learn useful attention supervision information in the meantime. During training, at each iteration, context words with the highest impact on sentiment prediction, identified based on their attention weights or gradients, are extracted as words with active/misleading influence on the correct/incorrect prediction for each instance. Words extracted in this way are masked for subsequent iterations. To exploit these extracted words for refining ABSA models, we augment the conventional training objective with a regularization term that encourages ABSA models to not only take full advantage of the extracted active context words but also decrease the weights of those misleading words. We integrate the proposed approach into three state-of-the-art neural ABSA models. Experiment results and in-depth analyses show that our approach yields better attention results and significantly enhances the performance of all three models. We release the source code and trained models at https://github.com/DeepLearnXMU/PSSAttention.


page 1

page 2

page 3

page 4


Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis

In aspect-level sentiment classification (ASC), it is prevalent to equip...

Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is to predict the sentiment polar...

Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification is a crucial problem in fine-grain...

KESA: A Knowledge Enhanced Approach For Sentiment Analysis

Though some recent works focus on injecting sentiment knowledge into pre...

Context-aware Embedding for Targeted Aspect-based Sentiment Analysis

Attention-based neural models were employed to detect the different aspe...

Pruning and Sparsemax Methods for Hierarchical Attention Networks

This paper introduces and evaluates two novel Hierarchical Attention Net...

Explaining a Neural Attention Model for Aspect-Based Sentiment Classification Using Diagnostic Classification

Many high performance machine learning models for Aspect-Based Sentiment...

Please sign up or login with your details

Forgot password? Click here to reset