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Cross-Domain and Cross-Language Irony Detection: The Impact of Bias on Models’ Generalization

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Natural Language Processing and Information Systems (NLDB 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13913))

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Abstract

Irony is a complex linguistic phenomenon that has been extensively studied in computational linguistics across many languages. Existing research has relied heavily on annotated corpora, which are inherently biased due to their creation process. This study focuses on the problem of bias in cross-domain and cross-language irony detection and aims to identify the extent of topic bias in benchmark corpora and how it affects the generalization of models across domains and languages (English, Spanish, and Italian). Our findings offer a first insight into this issue and showed that mitigating the topic bias in these corpora improves the generalization of models beyond their training data. These results have important implications for the development of robust models in the analysis of ironic language.

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Notes

  1. 1.

    https://github.com/VinAIResearch/BERTweet.

  2. 2.

    https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased.

  3. 3.

    https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0.

  4. 4.

    https://huggingface.co/bert-base-multilingual-uncased.

  5. 5.

    https://github.com/reynierortegabueno86/NLDB2023-Irony-Paper.

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Acknowledgement

The work of Ortega-Bueno and Rosso was in the framework of the FairTransNLP research project (PID2021-124361OB-C31) funded by MCIN/AEI/10.13039/501100011033 and by ERDF, EU A way of making Europe.

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Authors and Affiliations

  1. PRHLT Research Center, Universitat Politècnica de València, Camí de Vera S/N, Valencia, Spain

    Reynier Ortega-Bueno & Paolo Rosso

  2. Università degli Studi di MILANO-BICOCCA, Milan, Italy

    Elisabetta Fersini

Authors
  1. Reynier Ortega-Bueno
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  2. Paolo Rosso
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  3. Elisabetta Fersini
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Corresponding author

Correspondence to Reynier Ortega-Bueno .

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Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Derby, Derby, UK

    Farid Meziane

  3. Oakland University, Rochester, NY, USA

    Vijayan Sugumaran

  4. University of Derby, Derby, UK

    Warren Manning

  5. University of Derby, Derby, UK

    Stephan Reiff-Marganiec

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Ortega-Bueno, R., Rosso, P., Fersini, E. (2023). Cross-Domain and Cross-Language Irony Detection: The Impact of Bias on Models’ Generalization. In: Métais, E., Meziane, F., Sugumaran, V., Manning, W., Reiff-Marganiec, S. (eds) Natural Language Processing and Information Systems. NLDB 2023. Lecture Notes in Computer Science, vol 13913. Springer, Cham. https://doi.org/10.1007/978-3-031-35320-8_10

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