Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis

TL;DR

This paper reformulates aspect-based sentiment analysis as a conditional text generation task, leveraging pre-trained models to achieve state-of-the-art results in restaurant and urban neighborhood domains.

Contribution

It introduces a novel formulation of ABSA as a conditional text generation problem and demonstrates its effectiveness with fine-tuned pre-trained models.

Findings

Achieved new state-of-the-art results on benchmark datasets.

Validated the effectiveness of the conditional text generation approach.

Improved performance over traditional ABSA methods.

Abstract

Aspect-based sentiment analysis (ABSA) is an NLP task that entails processing user-generated reviews to determine (i) the target being evaluated, (ii) the aspect category to which it belongs, and (iii) the sentiment expressed towards the target and aspect pair. In this article, we propose transforming ABSA into an abstract summary-like conditional text generation task that uses targets, aspects, and polarities to generate auxiliary statements. To demonstrate the efficacy of our task formulation and a proposed system, we fine-tune a pre-trained model for conditional text generation tasks to get new state-of-the-art results on a few restaurant domains and urban neighborhoods domain benchmark datasets.