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

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

URL: http://arxiv.org/abs/2310.16528v1
Date: Wed, 25 Oct 2023 10:22:49 GMT
Title: CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval
Authors: Jind\v{r}ich Helcl and Jind\v{r}ich Libovick\'y
Abstract summary: We present the Charles University system for the MRL2023 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.
Score: 5.97515243922116
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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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