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CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval

arXiv 2310.16528 ↗First posted 25 Oct 2023cs.CLcited by 0

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Abstract (arXiv)

We present the Charles University system for the MRL~2023 Shared Task on Multi-lingual Multi-task Information Retrieval. The goal of the shared task was to develop systems for named entity recognition and question answering in several under-represented languages. Our solutions to both subtasks rely on the translate-test approach. We first translate the unlabeled examples into English using a multilingual machine translation model. Then, we run inference on the translated data using a strong task-specific model. Finally, we project the labeled data back into the original language. To keep the inferred tags on the correct positions in the original language, we propose a method based on scoring the candidate positions using a label-sensitive translation model. In both settings, we experiment with finetuning the classification models on the translated data. However, due to a domain mismatch between the development data and the shared task validation and test sets, the finetuned models could not outperform our baselines.

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References · 20

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8 more
  1. 2021Consistency Regularization for Cross-Lingual Fine-Tuningcited by 41
  2. 2023Revisiting Machine Translation for Cross-lingual Classificationcited by 41
  3. 2022T-NER: An All-Round Python Library for Transformer-based Named Entity Recognitioncited by 37
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