CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval
This work addresses information retrieval for under-represented languages, but it is incremental as it applies existing methods to a new shared task.
The authors tackled the MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval by developing a system for named entity recognition and question answering in under-represented languages using a translate-test approach, but their finetuned models did not outperform baselines due to domain mismatch.
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.