Text Understanding with the Attention Sum Reader Network

Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Ensemble of our models sets new state of the art on all evaluated datasets.Comment: Presented at ACL 201

Development of multiple media documents

Development of documents in multiple media involves activities in three different fields, the technical, the discoursive and the procedural. The major development problems of artifact complexity, cognitive processes, design basis and working context are located where these fields overlap. Pending the emergence of a unified approach to design, any method must allow for development at the three levels of discourse structure, media disposition and composition, and presentation. Related work concerned with generalised discourse structures, structured documents, production methods for existing multiple media artifacts, and hypertext design offer some partial forms of assistance at different levels. Desirable characteristics of a multimedia design method will include three phases of production, a variety of possible actions with media elements, an underlying discoursive structure, and explicit comparates for review