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        <namePart type="given">Pavel</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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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.</abstract>
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%0 Conference Proceedings
%T Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
%A Přibáň, Pavel
%A Prazak, Ondrej
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F priban-prazak-2023-improving
%X 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.
%U https://aclanthology.org/2023.ranlp-1.96
%P 888-897

Markdown (Informal)

[Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model](https://aclanthology.org/2023.ranlp-1.96) (Přibáň & Prazak, RANLP 2023)

• Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model (Přibáň & Prazak, RANLP 2023)

ACL

• Pavel Přibáň and Ondrej Prazak. 2023. Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 888–897, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.