ViMMRC 2.0——增强越南文学文本的机器阅读理解
ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text
AI总结：
本文提出 ViMMRC 2.0 越南文学阅读理解数据集，包含 699 篇篇章和 5,273 个非固定选项问题，并结合多步注意力网络与自然语言推理的多阶段方法提升模型性能，同时揭示隐式上下文理解仍是主要挑战。
摘要：
机器阅读理解近年来一直是一项有趣且具有挑战性的任务，其目的是从文本中提取有用信息。为了使计算机具备理解阅读文本并回答相关信息的能力，我们引入了 ViMMRC 2.0——这是先前 ViMMRC 的扩展，用于越南教科书中的多项选择阅读理解任务，这些教科书包含面向一年级至十二年级学生的阅读文章。该数据集包含 699 篇散文和诗歌阅读篇章，以及 5,273 个问题。新数据集中的问题不再像先前版本那样固定为四个选项。此外，问题的难度有所提高，这对模型找到正确选项构成了挑战。计算机必须理解阅读篇章的整个上下文、问题以及每个选项的内容，才能提取出正确答案。因此，我们提出了一种多阶段方法，将多步注意力网络（MAN）与自然语言推理（NLI）任务相结合，以增强阅读理解模型的性能。然后，我们在新数据集和 ViMMRC 1.0 上将所提出的方法与基线 BERTology 模型进行了比较。根据错误分析的结果，我们发现阅读理解模型面临的挑战在于理解文本中的隐式上下文并将其联系起来以找到正确答案。最后，我们希望我们的新数据集能够推动进一步的研究，以增强计算机理解越南语的能力。
英文摘要：
Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose a multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. From the results of the error analysis, we found that the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research to enhance the ability of computers to understand the Vietnamese language.
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。
