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.

我们提出了查理斯大学关于 MRL~2023 多语言多任务信息检索的系统。共享任务的目标是为多个代表性不足的语言开发命名实体识别和问题回答系统。我们针对两个子任务的解决方案都依赖于翻译 - 测试方法。首先，我们使用多语言机器翻译模型将无标签的示例转化为英文。然后，我们使用强大的任务特定模型对翻译后的数据进行推断。最后，我们将标记数据投射回原始语言。为了保持原始语言中推断标记的正确位置，我们提出了一种基于标签敏感翻译模型评分候选位置的方法。在两种情况下，我们尝试在翻译后的数据上微调分类模型。然而，由于开发数据和共享任务验证和测试集之间的领域不匹配，微调模型无法超越我们的基准线。