Text Understanding with the Attention Sum Reader Network
Rudolf Kadlec, Martin Schmid, Ondřej Bajgar, Jan Kleindienst · 2016
Several large cloze-style context-questionanswer 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 questionanswering 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.