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

Pavel Přibáň; Ondřej Pražák

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arXiv · 2307.14785

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

Pavel Přibáň·Ondřej Pražák

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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.

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BibTeX RIS

Pavel Přibáň, Ondřej Pražák. 2023-07-27. Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model. https://arxiv.org/abs/2307.14785

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