El Departamento de Nosotros：机器翻译语料库如何影响MRC任务中的语言模型
El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks
摘要：
大规模语言模型的预训练需要大量的文本语料。英语学习型语料库中的语言资源日益丰富。然而，资源较少的语言及其单语言和多语言LM通常难以获得更大的数据集。在这种情况下，典型的方法意味着使用英语语料库到目标语言的机器翻译。在这项工作中，我们研究了应用直接翻译的语料库微调LM下游自然语言处理任务的注意事项，并证明了精心策划与后处理导致提高性能和整体LM的鲁棒性。在实证评估中，我们在用户和系统层面上对直接翻译的西班牙SQuAD数据集进行了比较。在XQuAD和MLQA迁移学习评估问答任务上的进一步实验结果表明，就精确匹配分数而言，多语言LM可能对机器翻译工件表现出更大的弹性。
英文摘要：
Pre-training large-scale language models (LMs) requires huge amounts of text corpora. LMs for English enjoy ever growing corpora of diverse language resources. However, less resourced languages and their mono- and multilingual LMs often struggle to obtain bigger datasets. A typical approach in this case implies using machine translation of English corpora to a target language. In this work, we study the caveats of applying directly translated corpora for fine-tuning LMs for downstream natural language processing tasks and demonstrate that careful curation along with post-processing lead to improved performance and overall LMs robustness. In the empirical evaluation, we perform a comparison of directly translated against curated Spanish SQuAD datasets on both user and system levels. Further experimental results on XQuAD and MLQA transfer-learning evaluation question answering tasks show that presumably multilingual LMs exhibit more resilience to machine translation artifacts in terms of the exact match score.
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。
