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        <title>Task and Sentiment Adaptation for Appraisal Tagging</title>
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        <namePart type="given">Xiuzhen</namePart>
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    <name type="personal">
        <namePart type="given">Jennifer</namePart>
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            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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    <abstract>The Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions and has contributed to sentiment analysis and opinion mining. As developing appraisal-annotated resources requires tagging of several dimensions with distinct semantic taxonomies, it has been primarily conducted manually by human experts through expensive and time-consuming processes. In this paper, we study how to automatically identify and annotate text segments for appraisal. We formulate the problem as a sequence tagging problem and propose novel task and sentiment adapters based on language models for appraisal tagging. Our model, named Adaptive Appraisal (Aˆ2), achieves superior performance than baseline adapter-based models and other neural classification models, especially for cross-domain and cross-language settings. Source code for Aˆ2 is available at: https://github.com/ltian678/AA-code.git</abstract>
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    <identifier type="doi">10.18653/v1/2023.eacl-main.144</identifier>
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        <date>2023-05</date>
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            <start>1960</start>
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%0 Conference Proceedings
%T Task and Sentiment Adaptation for Appraisal Tagging
%A Tian, Lin
%A Zhang, Xiuzhen
%A Kim, Myung Hee
%A Biggs, Jennifer
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F tian-etal-2023-task
%X The Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions and has contributed to sentiment analysis and opinion mining. As developing appraisal-annotated resources requires tagging of several dimensions with distinct semantic taxonomies, it has been primarily conducted manually by human experts through expensive and time-consuming processes. In this paper, we study how to automatically identify and annotate text segments for appraisal. We formulate the problem as a sequence tagging problem and propose novel task and sentiment adapters based on language models for appraisal tagging. Our model, named Adaptive Appraisal (Aˆ2), achieves superior performance than baseline adapter-based models and other neural classification models, especially for cross-domain and cross-language settings. Source code for Aˆ2 is available at: https://github.com/ltian678/AA-code.git
%R 10.18653/v1/2023.eacl-main.144
%U https://aclanthology.org/2023.eacl-main.144
%U https://doi.org/10.18653/v1/2023.eacl-main.144
%P 1960-1970

Markdown (Informal)

[Task and Sentiment Adaptation for Appraisal Tagging](https://aclanthology.org/2023.eacl-main.144) (Tian et al., EACL 2023)

• Task and Sentiment Adaptation for Appraisal Tagging (Tian et al., EACL 2023)

ACL

• Lin Tian, Xiuzhen Zhang, Myung Hee Kim, and Jennifer Biggs. 2023. Task and Sentiment Adaptation for Appraisal Tagging. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1960–1970, Dubrovnik, Croatia. Association for Computational Linguistics.