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.
翻译:问题解答(QA)是最重要的自然语言处理(NLP)任务之一,目的是利用NLP技术在大规模非结构化的基础上对一个特定问题产生相应的答案。随着深入学习的发展,正在提出越来越多的具有挑战性的质量解答数据集,解决这些数据集的新方法也正在出现。在本文中,我们调查了在深层次学习时代释放出来的有影响力的质量保证数据集。具体地说,我们首先介绍两个最常见的质量保证任务――文字问答和直观回答——分别涵盖最具代表性的数据集,然后提出当前质量解答研究的一些挑战。