CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task
  Information Retrieval
Jindřich Helcl Jindřich Libovický
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.