VLSP 2021 - ViMRC挑战赛：越南语机器阅读理解
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension
AI总结：
针对现有越南语MRC数据集仅覆盖可回答问题的不足，发布UIT-ViQuAD 2.0基准数据集并举办VLSP 2021越南语MRC挑战赛，公布参赛情况、参赛方法及最高成绩，推动越南语相关NLP研究。
摘要：
机器阅读理解（MRC）是自然语言理解领域的新兴研究方向之一，其任务是基于文本数据为人类问题寻找答案。现有的越南语MRC研究数据集仅关注可回答的问题。但在实际中，问题可能是无法回答的，即给定文本数据中未给出正确答案。为弥补这一不足，我们为研究界提供了一个名为UIT-ViQuAD 2.0的基准数据集，用于评估越南语的MRC任务和问答系统。我们将UIT-ViQuAD 2.0用作第八届越南语言与语音处理研讨会（VLSP 2021）越南语MRC挑战赛的基准数据集。该任务吸引了来自34所高校及其他机构的77支参赛队伍。本文介绍了挑战赛的组织细节、共享任务参与者所采用方法的概述以及比赛结果。在私有测试集上，最高性能为F1值77.24%、精确匹配率67.43%。排名前三的团队提出的越南语MRC系统均使用XLM-RoBERTa——一种基于Transformer架构的强大预训练语言模型。UIT-ViQuAD 2.0数据集可激励研究人员进一步探索越南语机器阅读理解任务及相关任务，如问答、问题生成和自然语言推理。
英文摘要：
One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 77.24% in F1-score and 67.43% in Exact Match on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further explore the Vietnamese machine reading comprehension task and related tasks such as question answering, question generation, and natural language inference.
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。
