Although general question answering has been well explored in recent years, temporal question answering is a task which has not received as much focus. Our work aims to leverage a popular approach used for general question answering, answer extraction, in order to find answers to temporal questions within a paragraph. To train our model, we propose a new dataset, inspired by SQuAD, specifically tailored to provide rich temporal information. We chose to adapt the corpus WikiWars, which contains several documents on history's greatest conflicts. Our evaluation shows that a deep learning model trained to perform pattern matching, often used in general question answering, can be adapted to temporal question answering, if we accept to ask questions whose answers must be directly present within a text.

翻译：尽管通用问答系统近年来已得到充分探索，但时间问答作为一个任务尚未受到同等关注。本研究旨在利用通用问答中广泛采用的答案抽取方法，从段落中定位时间性问题的答案。为训练模型，我们提出一个受SQuAD启发的新数据集，专门设计用于提供丰富的时间信息。我们选择改编WikiWars语料库，该库包含多篇关于历史上重大冲突的文档。评估结果表明：若将问题限定为答案必须直接存在于文本中的形式，则常用于通用问答的、经过模式匹配训练的深度学习模型可适配至时间问答任务。