Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis

TL;DR

This paper introduces ATOSS, a sentence splitting model that simplifies complex sentences to improve the extraction of sentiment quadruplets in Aspect-Based Sentiment Analysis, leading to better performance in key tasks.

Contribution

The paper presents a novel sentence splitter that enhances ABSA tasks by simplifying sentence structures without altering existing models.

Findings

ATOSS outperforms existing methods in ASQP and ACOS tasks.

Simplifying sentences improves sentiment quadruplet extraction accuracy.

The approach is plug-and-play and retains original model parameters.

Abstract

In the domain of Aspect-Based Sentiment Analysis (ABSA), generative methods have shown promising results and achieved substantial advancements. However, despite these advancements, the tasks of extracting sentiment quadruplets, which capture the nuanced sentiment expressions within a sentence, remain significant challenges. In particular, compound sentences can potentially contain multiple quadruplets, making the extraction task increasingly difficult as sentence complexity grows. To address this issue, we are focusing on simplifying sentence structures to facilitate the easier recognition of these elements and crafting a model that integrates seamlessly with various ABSA tasks. In this paper, we propose Aspect Term Oriented Sentence Splitter (ATOSS), which simplifies compound sentence into simpler and clearer forms, thereby clarifying their structure and intent. As a plug-and-play module, this approach retains the parameters of the ABSA model while making it easier to identify essential intent within input sentences. Extensive experimental results show that utilizing ATOSS outperforms existing methods in both ASQP and ACOS tasks, which are the primary tasks for extracting sentiment quadruplets.