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Cross-Lingual Annotation Projection for Argument Mining in Portuguese

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Progress in Artificial Intelligence (EPIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12981))

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Abstract

While Argument Mining has seen increasing success in monolingual settings, especially for the English language, other less-resourced languages are still lagging behind. In this paper, we build a Portuguese projected version of the Persuasive Essays corpus and evaluate it both intrinsically (through back-projection) and extrinsically (in a sequence tagging task). To build the corpus, we project the token-level annotations into a new Portuguese version using translations and respective alignments. Intrinsic evaluation entails rebuilding the English corpus using back alignment and back projection from the Portuguese version, comparing against the original English annotations. For extrinsic evaluation, we assess and compare the performance of machine learning models on several language variants of the corpus (including the Portuguese one), following both in-language/projection training and direct transfer. Our evaluation highlights the quality of the generated corpus. Experimental results show the effectiveness of the projection approach, while providing competitive baselines for the Portuguese version of the corpus. The corpus and code are available (https://github.com/AfonsoSalgadoSousa/argumentation_mining_pt).

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Notes

  1. 1.

    https://github.com/UKPLab/acl2017-neural_end2end_am/tree/master/data/conll/Paragraph_Level.

  2. 2.

    https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE.

  3. 3.

    https://opus.nlpl.eu/.

  4. 4.

    https://github.com/UKPLab/coling2018-xling_argument_mining/tree/master/data/AllData/MT/PE.

  5. 5.

    https://huggingface.co/transformers/pretrained_models.html, model id “bert-base-multilingual-cased”.

  6. 6.

    https://github.com/XuezheMax/NeuroNLP2.

  7. 7.

    https://github.com/facebookresearch/MUSE.

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Acknowledgment

This research is supported by LIACC (FCT/UID/CEC/0027/2020) and by project DARGMINTS (POCI/01/0145/FEDER/031460), funded by Fundação para a Ciência e a Tecnologia (FCT). Gil Rocha is supported by a PhD studentship (with reference SFRH/BD/140125/2018) from FCT.

Author information

Authors and Affiliations

  1. Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Afonso Sousa, Bernardo Leite, Gil Rocha & Henrique Lopes Cardoso

  2. Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC), Porto, Portugal

    Bernardo Leite, Gil Rocha & Henrique Lopes Cardoso

Authors
  1. Afonso Sousa
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  2. Bernardo Leite
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  3. Gil Rocha
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  4. Henrique Lopes Cardoso
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Corresponding author

Correspondence to Henrique Lopes Cardoso .

Editor information

Editors and Affiliations

  1. ISEP/GECAD, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  2. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Francisco S. Melo

  3. DETI/IEETA, University of Aveiro, Aveiro, Portugal

    Nuno Lau

  4. FEUP/LIACC, University of Porto, Porto, Portugal

    Henrique Lopes Cardoso

  5. FEUP/LIACC, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Sousa, A., Leite, B., Rocha, G., Lopes Cardoso, H. (2021). Cross-Lingual Annotation Projection for Argument Mining in Portuguese. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_59

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  • DOI: https://doi.org/10.1007/978-3-030-86230-5_59

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