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. ...

Question generation is the parallel task of question answering, where given an input context and, optionally, answer, goal to generate a relevant and fluent natural language question. Although recent works on have experienced success by utilizing sequence-to-sequence models, there need for models handle increasingly complex contexts produce detailed questions. Multi-hop more challenging that ai...

Question generation is the parallel task of question answering, where given an input context and optionally, answer, goal to generate a relevant fluent natural language question. Although recent works on have experienced success by utilizing sequence-to-sequence models, there need for models handle increasingly complex contexts with producing elaborate questions. Multi-hop more challenging that...