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        <title>Complaint Identification in Social Media with Transformer Networks</title>
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        <namePart type="given">Mali</namePart>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic information from topics or sentiment for complaint prediction has yet to be explored. In this paper, we evaluate a battery of neural models underpinned by transformer networks which we subsequently combine with linguistic information. Experiments on a publicly available data set of complaints demonstrate that our models outperform previous state-of-the-art methods by a large margin achieving a macro F1 up to 87.</abstract>
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%0 Conference Proceedings
%T Complaint Identification in Social Media with Transformer Networks
%A Jin, Mali
%A Aletras, Nikolaos
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F jin-aletras-2020-complaint
%X Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic information from topics or sentiment for complaint prediction has yet to be explored. In this paper, we evaluate a battery of neural models underpinned by transformer networks which we subsequently combine with linguistic information. Experiments on a publicly available data set of complaints demonstrate that our models outperform previous state-of-the-art methods by a large margin achieving a macro F1 up to 87.
%R 10.18653/v1/2020.coling-main.157
%U https://aclanthology.org/2020.coling-main.157
%U https://doi.org/10.18653/v1/2020.coling-main.157
%P 1765-1771

Markdown (Informal)

[Complaint Identification in Social Media with Transformer Networks](https://aclanthology.org/2020.coling-main.157) (Jin & Aletras, COLING 2020)

• Complaint Identification in Social Media with Transformer Networks (Jin & Aletras, COLING 2020)

ACL

• Mali Jin and Nikolaos Aletras. 2020. Complaint Identification in Social Media with Transformer Networks. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1765–1771, Barcelona, Spain (Online). International Committee on Computational Linguistics.