CUNI 提交至 MRL 2023 多语言多任务信息检索共享任务
CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval
AI总结：
本文提出采用“先翻译后测试”策略处理多语言命名实体识别与问答任务，并设计标签敏感翻译评分方法以对齐标签位置，但微调受领域不匹配限制未能超过基线。
摘要：
我们介绍查理大学系统参加 MRL 2023 多语言多任务信息检索共享任务的情况。该共享任务的目标是为若干代表性不足的语言开发命名实体识别和问答系统。我们对两个子任务的解决方案都依赖“先翻译后测试”方法。我们首先使用多语言机器翻译模型将未标注样本翻译成英语，然后使用强大的任务专用模型对翻译后的数据进行推理，最后将标注数据投影回原始语言。为使推断出的标签保持在原始语言中的正确位置，我们提出一种基于使用标签敏感翻译模型对候选位置进行评分的方法。在两种设置中，我们都尝试在翻译后的数据上微调分类模型。然而，由于开发数据与共享任务验证集和测试集之间存在领域不匹配，微调后的模型未能超越我们的基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
