Machine comprehension (MC) and question answering (QA) are crucial tasks in natural language understanding. Training deep neural network-based QA models has become practical upon the recent release of the Stanford Question Answering Dataset (SQuAD), a significantly larger dataset of question-answer pairs created by humans on a set of Wikipedia articles [1]. In this paper, we propose an end-to-e...

As part of the task of automated question answering from a large collection of text documents, the reduction of the search space to a smaller set of document passages that are actually searched for answers constitutes a difficult but rewarding research issue. We propose a set of precision-enhancing filters for passage retrieval based on semantic constraints detected in the submitted questions. ...