Answer type validation in question answering systems

International audienceIn open-domain question-answering systems, numerous questions wait for answers of an explicit type. For example, the question ``Which president succeeded Jacques Chirac?'' requires an instance of president as the answer. The method we present in this article aims at verifying that an answer given by a system corresponds to the given type. This verification is done by combining criteria provided by different methods dedicated to verifying the appropriateness between an answer and a type. The first types of criteria are statistical and compute the presence rate of both the answer and the type in documents, other criteria rely on named entity recognizers and the last criteria are based on the use of Wikipedia

Structure Learning for Neural Module Networks

Neural Module Networks, originally proposed for the task of visual question answering, are a class of neural network architectures that involve human-specified neural modules, each designed for a specific form of reasoning. In current formulations of such networks only the parameters of the neural modules and/or the order of their execution is learned. In this work, we further expand this approach and also learn the underlying internal structure of modules in terms of the ordering and combination of simple and elementary arithmetic operators. Our results show that one is indeed able to simultaneously learn both internal module structure and module sequencing without extra supervisory signals for module execution sequencing. With this approach, we report performance comparable to models using hand-designed modules