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. ...

The aim of this study was to identify the underlying semantics of health consumers' questions and physicians' answers in order to analyze the semantic patterns within these texts. We manually identified semantic relationships within question-answer pairs from Ask-the-Doctor Web sites. Identification of the semantic relationship instances within the texts was based on the relationship classes an...

The quality of teachers has influence on the quality of educational services. Nurturing theteachers is actually a preoccupation for the educational organizations. The aim of our educationsystem is to produce successful, well-prepared citizens; therefore, careful examination ofindividuals who teach at all levels is a critical step in meeting that goal. The current studyin...

Today, computer vision systems are tested by their accuracy in detecting and localizing instances of objects. As an alternative, and motivated by the ability of humans to provide far richer descriptions and even tell a story about an image, we construct a "visual Turing test": an operator-assisted device that produces a stochastic sequence of binary questions from a given test image. The query ...