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

• URL: http://arxiv.org/abs/2307.14785v1
• Date: Thu, 27 Jul 2023 11:28:16 GMT
• Title: Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
• Authors: Pavel P\v{r}ib\'a\v{n} and Ond\v{r}ej Pra\v{z}\'ak
• Abstract summary: This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) 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 evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models.
• Score: 6.85316573653194
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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