Abstract
Utilisation of multilingual language models such as mBERT and XLM-RoBERTa has increasingly gained attention in recent work by exploiting the multilingualism of such models in different downstream tasks across different languages. However, performance degradation is expected in transfer learning across languages compared to monolingual performance although it is an acceptable trade-off considering the sparsity of resources and lack of available training data in low-resource languages. In this work, we study the effect of machine translation on the cross-lingual transfer learning in a crisis event classification task. Our experiments include measuring the effect of machine-translating the test data into the source language and vice versa. We evaluated and compared the performance in terms of accuracy and F1-Score. The results show that translating the source data into the target language improves the prediction accuracy by 14.8% and the Weighted Average F1-Score by 19.2% when compared to zero-shot transfer to an unseen language.
Original language | English |
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Title of host publication | Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing |
Editors | Ruslan Mitkov, Galia Angelova |
Publisher | Incoma Ltd |
Pages | 22-31 |
Number of pages | 10 |
ISBN (Electronic) | 9789544520922 |
DOIs | |
Publication status | Published - 6 Sept 2023 |
Event | 2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023 - Varna, Bulgaria Duration: 4 Sept 2023 → 6 Sept 2023 |
Publication series
Name | International Conference Recent Advances in Natural Language Processing |
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Publisher | Incoma Ltd |
ISSN (Print) | 1313-8502 |
ISSN (Electronic) | 2603-2813 |
Conference
Conference | 2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023 |
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Country/Territory | Bulgaria |
City | Varna |
Period | 4/09/23 → 6/09/23 |
Bibliographical note
Publisher Copyright:© 2023 Incoma Ltd. All rights reserved.
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Artificial Intelligence
- Electrical and Electronic Engineering