Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
This work addresses sentiment analysis for specific aspects in text, showing incremental improvements with a new model integration.
The paper tackled improving Aspect-Based Sentiment Analysis (ABSA) by using semantic information from a novel end-to-end Semantic Role Labeling model, achieving new state-of-the-art results on Czech ABSA and performance gains in English.
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.