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Multilingual Epidemic Event Extraction

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Towards Open and Trustworthy Digital Societies (ICADL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13133))

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Abstract

In this paper, we focus on epidemic event extraction in multilingual and low-resource settings. The task of extracting epidemic events is defined as the detection of disease names and locations in a document. We experiment with a multilingual dataset comprising news articles from the medical domain with diverse morphological structures (Chinese, English, French, Greek, Polish, and Russian). We investigate various Transformer-based models, also adopting a two-stage strategy, first finding the documents that contain events and then performing event extraction. Our results show that error propagation to the downstream task was higher than expected. We also perform an in-depth analysis of the results, concluding that different entity characteristics can influence the performance. Moreover, we perform several preliminary experiments for the low-resourced languages present in the dataset using the mean teacher semi-supervised technique. Our findings show the potential of pre-trained language models benefiting from the incorporation of unannotated data in the training process.

This work has been supported by the European Union’s Horizon 2020 research and innovation program under grants 770299 (NewsEye) and 825153 (Embeddia). It has also been supported by the French Embassy in Kenya and the French Foreign Ministry.

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Notes

  1. 1.

    The DAnIEL dataset is available at https://daniel.greyc.fr/public/index.php?a=corpus.

  2. 2.

    The token-level annotated dataset is available at https://bit.ly/3kUQcXD.

  3. 3.

    For this model, we used the parameters recommended in [11].

  4. 4.

    https://huggingface.co/bert-base-multilingual-cased. This model was pre-trained on the top 104 languages having the largest Wikipedia edition using a masked language modeling (MLM) objective.

  5. 5.

    https://huggingface.co/bert-base-multilingual-uncased. This model was pre-trained on the top 102 languages having the largest Wikipedia editions using a masked language modeling (MLM) objective.

  6. 6.

    XLM-RoBERTa-base was trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages.

  7. 7.

    https://jku-vds-lab.at/tools/upset/.

  8. 8.

    The code [16] is available here: https://github.com/neulab/InterpretEval..

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Author information

Authors and Affiliations

  1. L3i, University of La Rochelle, 17000, La Rochelle, France

    Stephen Mutuvi, Emanuela Boros & Antoine Doucet

  2. Multimedia University of Kenya, Nairobi, Kenya

    Stephen Mutuvi & Moses Odeo

  3. STIH Lab, Sorbonne University, 75006, Paris, France

    Gaël Lejeune

  4. University of Innsbruck, 6020, Innsbruck, Austria

    Adam Jatowt

Authors
  1. Stephen Mutuvi
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  2. Emanuela Boros
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  4. Gaël Lejeune
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Correspondence to Stephen Mutuvi .

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Editors and Affiliations

  1. National Taiwan Normal University, Taipei, Taiwan

    Hao-Ren Ke

  2. Nanyang Technological University, Singapore, Singapore

    Chei Sian Lee

  3. Kyoto University, Kyoto, Japan

    Kazunari Sugiyama

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Mutuvi, S., Boros, E., Doucet, A., Lejeune, G., Jatowt, A., Odeo, M. (2021). Multilingual Epidemic Event Extraction. In: Ke, HR., Lee, C.S., Sugiyama, K. (eds) Towards Open and Trustworthy Digital Societies. ICADL 2021. Lecture Notes in Computer Science(), vol 13133. Springer, Cham. https://doi.org/10.1007/978-3-030-91669-5_12

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  • DOI: https://doi.org/10.1007/978-3-030-91669-5_12

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