VlogQA：面向越南语口语机器阅读理解的任务、数据集与基线模型
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension
AI总结：
本文提出越南语口语机器阅读理解数据集 VlogQA，基于 YouTube 美食与旅行转录文档构建 10,076 个问答对，并通过深度学习基线模型验证了口语 MRC 的进展与挑战。
摘要：
本文介绍了面向机器阅读理解（MRC）任务的越南语口语语料库的开发过程，并就使用真实世界数据进行机器阅读理解任务所面临的挑战与机遇提供了见解。现有的越南语 MRC 语料库主要关注正式书面文档，如维基百科文章、在线报纸或教科书。相比之下，VlogQA 包含 10,076 个问答对，这些问答对基于 1,230 份来源于 YouTube 的转录文档——YouTube 是一个用户上传内容的广泛来源，涵盖美食和旅行主题。通过捕捉越南语母语者在自然场景中的口语表达，这一在越南语研究中被忽视的冷门领域，该语料库为未来越南语阅读理解任务的研究提供了宝贵资源。在性能评估方面，我们的深度学习模型在测试集上取得了最高 75.34% 的 F1 分数，表明越南语口语数据的机器阅读理解取得了显著进展。在 EM 方面，我们取得的最高分数为 53.97%，这反映了处理口语内容的挑战，并凸显了进一步改进的必要性。
英文摘要：
This paper presents the development process of a Vietnamese spoken language corpus for machine reading comprehension (MRC) tasks and provides insights into the challenges and opportunities associated with using real-world data for machine reading comprehension tasks. The existing MRC corpora in Vietnamese mainly focus on formal written documents such as Wikipedia articles, online newspapers, or textbooks. In contrast, the VlogQA consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube -- an extensive source of user-uploaded content, covering the topics of food and travel. By capturing the spoken language of native Vietnamese speakers in natural settings, an obscure corner overlooked in Vietnamese research, the corpus provides a valuable resource for future research in reading comprehension tasks for the Vietnamese language. Regarding performance evaluation, our deep-learning models achieved the highest F1 score of 75.34% on the test set, indicating significant progress in machine reading comprehension for Vietnamese spoken language data. In terms of EM, the highest score we accomplished is 53.97%, which reflects the challenge in processing spoken-based content and highlights the need for further improvement.
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。
