Skip to main content
SpringerLink
Log in

Cross-Lingual Annotation Projection for Argument Mining in Portuguese

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2021)

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

Included in the following conference series:

  • 1919 Accesses

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).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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.

References

  1. Ajjour, Y., Chen, W.F., Kiesel, J., Wachsmuth, H., Stein, B.: Unit segmentation of argumentative texts. In: Proceedings of the 4th Workshop on Argument Mining, pp. 118–128. ACL (September 2017)

    Google Scholar 

  2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations (Conference Track Proceedings), ICLR 2015, San Diego, CA, USA, 7–9 May 2015 (2015)

    Google Scholar 

  3. Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the ACL, pp. 8440–8451. ACL (July 2020)

    Google Scholar 

  4. Conneau, A., Lample, G., Ranzato, M., Denoyer, L., Jégou, H.: Word translation without parallel data. arXiv preprint arXiv:1710.04087 (2017)

  5. Das, D., Petrov, S.: Unsupervised part-of-speech tagging with bilingual graph-based projections. In: Proceedings of the 49th Annual Meeting of the ACL: Human Language Technologies, pp. 600–609. ACL (June 2011)

    Google Scholar 

  6. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the ACL: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, Volume 1 (Long and Short Papers), pp. 4171–4186. ACL (2019)

    Google Scholar 

  7. Dyer, C., Chahuneau, V., Smith, N.A.: A simple, fast, and effective reparameterization of IBM model 2. In: Proceedings of the 2013 Conference of the North American Chapter of the ACL: Human Language Technologies, pp. 644–648. ACL (June 2013)

    Google Scholar 

  8. Eckle-Kohler, J., Kluge, R., Gurevych, I.: On the role of discourse markers for discriminating claims and premises in argumentative discourse. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2236–2242. ACL (September 2015)

    Google Scholar 

  9. Eger, S., Daxenberger, J., Gurevych, I.: Neural end-to-end learning for computational argumentation mining. In: Proceedings of the 55th Annual Meeting of the ACL (Volume 1: Long Papers), pp. 11–22. ACL (July 2017)

    Google Scholar 

  10. Eger, S., Daxenberger, J., Stab, C., Gurevych, I.: Cross-lingual argumentation mining: machine translation (and a bit of projection) is all you need! In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 831–844. ACL (August 2018)

    Google Scholar 

  11. Garg, S., Peitz, S., Nallasamy, U., Paulik, M.: Jointly learning to align and translate with transformer models. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3–7 November 2019, pp. 4452–4461. ACL (2019)

    Google Scholar 

  12. Habernal, I., Gurevych, I.: Argumentation mining in user-generated web discourse. Comput. Linguist. 43(1), 125–179 (2017)

    Article  MathSciNet  Google Scholar 

  13. Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)

  14. Jalili Sabet, M., Dufter, P., Yvon, F., Schütze, H.: SimAlign: high quality word alignments without parallel training data using static and contextualized embeddings. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 1627–1643. ACL (November 2020)

    Google Scholar 

  15. Li, M., Geng, S., Gao, Y., Peng, S., Liu, H., Wang, H.: Crowdsourcing argumentation structures in Chinese hotel reviews. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 87–92 (2017)

    Google Scholar 

  16. Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the ACL (Volume 1: Long Papers), pp. 1064–1074. ACL (August 2016)

    Google Scholar 

  17. Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany (Volume 1: Long Papers), pp. 1064–1074. Association for Computational Linguistics (August 2016). https://doi.org/10.18653/v1/P16-1101. https://www.aclweb.org/anthology/P16-1101

  18. McDonald, R., Petrov, S., Hall, K.: Multi-source transfer of delexicalized dependency parsers. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 62–72. ACL (July 2011)

    Google Scholar 

  19. Nguyen, H., Litman, D.: Context-aware argumentative relation mining. In: Proceedings of the 54th Annual Meeting of the ACL (Volume 1: Long Papers), pp. 1127–1137. ACL (August 2016)

    Google Scholar 

  20. Och, F.J., Ney, H.: Improved statistical alignment models. In: Proceedings of the 38th Annual Meeting of the ACL, pp. 440–447. ACL (October 2000)

    Google Scholar 

  21. Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Comput. Linguist. 29(1), 19–51 (2003)

    Article  Google Scholar 

  22. Palau, R.M., Moens, M.F.: Argumentation mining: the detection, classification and structure of arguments in text. In: Proceedings of the 12th International Conference on Artificial Intelligence and Law, pp. 98–107. Association for Computing Machinery (2009)

    Google Scholar 

  23. Peldszus, A., Stede, M.: From argument diagrams to argumentation mining in texts: a survey. Int. J. Cogn. Inform. Nat. Intell. 7(1), 1–31 (2013)

    Article  Google Scholar 

  24. Pikuliak, M., Šimko, M., Bieliková, M.: Cross-lingual learning for text processing: a survey. Expert Syst. Appl. 165, 113765 (2021)

    Article  Google Scholar 

  25. Plank, B., Søgaard, A., Goldberg, Y.: Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss. In: Proceedings of the 54th Annual Meeting of the ACL (Volume 2: Short Papers), pp. 412–418. ACL (August 2016)

    Google Scholar 

  26. Rocha, G., Lopes Cardoso, H.: Towards a relation-based argument extraction model for argumentation mining. In: Camelin, N., Estève, Y., Martín-Vide, C. (eds.) SLSP 2017. LNCS (LNAI), vol. 10583, pp. 94–105. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68456-7_8

    Chapter  Google Scholar 

  27. Rocha, G., Stab, C., Lopes Cardoso, H., Gurevych, I.: Cross-lingual argumentative relation identification: from English to Portuguese. In: Proceedings of the 5th Workshop on Argument Mining, pp. 144–154. ACL (November 2018)

    Google Scholar 

  28. Stab, C., Gurevych, I.: Parsing argumentation structures in persuasive essays. Comput. Linguist. 43(3), 619–659 (2017)

    Article  MathSciNet  Google Scholar 

  29. Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4–9 December 2017, Long Beach, CA, USA. pp. 5998–6008 (2017)

    Google Scholar 

  30. Wang, H., Huang, Z., Dou, Y., Hong, Y.: Argumentation mining on essays at multi scales. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 5480–5493. International Committee on Computational Linguistics (December 2020)

    Google Scholar 

  31. Wolf, T., et al..: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38–45. Association for Computational Linguistics (October 2020). https://www.aclweb.org/anthology/2020.emnlp-demos.6

  32. Yang, S., Wang, Y., Chu, X.: A survey of deep learning techniques for neural machine translation. arXiv preprint arXiv:2002.07526 (2020)

  33. Yang, Z., Salakhutdinov, R., Cohen, W.W.: Transfer learning for sequence tagging with hierarchical recurrent networks. In: 5th International Conference on Learning Representations (Conference Track Proceedings), ICLR 2017, Toulon, France, 24–26 April 2017. OpenReview.net (2017)

    Google Scholar 

  34. Yarowsky, D., Ngai, G., Wicentowski, R.: Inducing multilingual text analysis tools via robust projection across aligned corpora. In: Proceedings of the 1st International Conference on Human Language Technology Research (2001)

    Google Scholar 

  35. Zhang, Y., Gaddy, D., Barzilay, R., Jaakkola, T.: Ten pairs to tag - multilingual POS tagging via coarse mapping between embeddings. In: Proceedings of the 2016 Conference of the North American Chapter of the ACL: Human Language Technologies, pp. 1307–1317. ACL (June 2016)

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernardo Leite
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gil Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Henrique Lopes Cardoso
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86229-9

  • Online ISBN: 978-3-030-86230-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation