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

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 stateof-the-art results on the Czech ABSA.