Machine Reading Comprehension (MRC) is a task that requires machine to
understand natural language and answer questions by reading a document. It is
the core of automatic response technology such as chatbots and automatized
customer supporting systems. We present Korean Question Answering
Dataset(KorQuAD), a large-scale Korean dataset for extractive machine reading
comprehension task. It consists of 70,000+ human generated question-answer
pairs on Korean Wikipedia articles. We release KorQuAD1.0 and launch a
challenge at this https URL to encourage the development of
multilingual natural language processing research.

该研究介绍了韩语问答数据集（KorQuAD），这是一个用于机器阅读理解的大型韩语数据集，有助于自动回答语音机器人和自动化客户支持系统的开发。

KorQuAD1.0：针对机器阅读理解的韩文问答数据集

KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension

A fundamental trade-off between effectiveness and efficiency needs to be
balanced when designing an online question answering system. Effectiveness
comes from sophisticated functions such as extractive machine reading
comprehension (MRC), while efficiency is obtained from improvements in
preliminary retrieval components such as candidate document selection and
paragraph ranking. Given the complexity of the real-world multi-document MRC
scenario, it is difficult to jointly optimize both in an end-to-end system. To
address this problem, we develop a novel deep cascade learning model, which
progressively evolves from the document-level and paragraph-level ranking of
candidate texts to more precise answer extraction with machine reading
comprehension. Specifically, irrelevant documents and paragraphs are first
filtered out with simple functions for efficiency consideration. Then we
jointly train three modules on the remaining texts for better tracking the
answer: the document extraction, the paragraph extraction and the answer
extraction. Experiment results show that the proposed method outperforms the
previous state-of-the-art methods on two large-scale multi-document benchmark
datasets, i.e., TriviaQA and DuReader. In addition, our online system can
stably serve typical scenarios with millions of daily requests in less than
50ms.

本研究提出一种深度串级学习模型来平衡在线问答系统的效能和效率之间的基本权衡，在多文档的 MRC 场景中取得更好的效果并能够在不到 50 毫秒内稳定地为数百万日请求提供服务。