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We present the Charles University system for the MRL~2023 Shared Task onMulti-lingual Multi-task Information Retrieval. The goal of the shared task wasto develop systems for named entity recognition and question answering inseveral under-represented languages. Our solutions to both subtasks rely on thetranslate-test approach. We first translate the unlabeled examples into Englishusing a multilingual machine translation model. Then, we run inference on thetranslated data using a strong task-specific model. Finally, we project thelabeled data back into the original language. To keep the inferred tags on thecorrect positions in the original language, we propose a method based onscoring the candidate positions using a label-sensitive translation model. Inboth settings, we experiment with finetuning the classification models on thetranslated data. However, due to a domain mismatch between the development dataand the shared task validation and test sets, the finetuned models could notoutperform our baselines.

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