Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model

TL;DR

This paper introduces an end-to-end Semantic Role Labeling model that enhances Aspect-Based Sentiment Analysis by effectively capturing semantic information, leading to improved performance and new state-of-the-art results in Czech.

Contribution

The paper proposes a novel end-to-end SRL model integrated with Transformer-based ABSA, demonstrating significant performance improvements and state-of-the-art results in Czech.

Findings

Improved ABSA performance in English and Czech.

Achieved new state-of-the-art results on Czech ABSA.

Effective extraction of semantic information enhances sentiment analysis.

Abstract

This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state. We believe that this end-to-end model is well-suited for our newly proposed models that incorporate semantic information. We evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models. Our combined models improve ABSA performance in both languages. Moreover, we achieved new state-of-the-art results on the Czech ABSA.