论文标题
现代问题回答数据集和基准：调查
Modern Question Answering Datasets and Benchmarks: A Survey
论文作者
论文摘要
问题回答（QA）是最重要的自然语言处理（NLP）任务之一。它旨在使用NLP技术根据大规模的非结构化语料库生成对给定问题的相应答案。随着深度学习的发展，正在提出越来越具有挑战性的质量检查数据集，并且许多用于解决它们的新方法也在出现。在本文中，我们研究了在深度学习时代发布的有影响力的质量检查数据集。具体来说，我们首先介绍两个最常见的质量保证任务 - 文本问题答案和视觉问题答案 - 分别涵盖最具代表性的数据集，然后给出质量检查研究的一些当前挑战。
Question Answering (QA) is one of the most important natural language processing (NLP) tasks. It aims using NLP technologies to generate a corresponding answer to a given question based on the massive unstructured corpus. With the development of deep learning, more and more challenging QA datasets are being proposed, and lots of new methods for solving them are also emerging. In this paper, we investigate influential QA datasets that have been released in the era of deep learning. Specifically, we begin with introducing two of the most common QA tasks - textual question answer and visual question answering - separately, covering the most representative datasets, and then give some current challenges of QA research.
