Papers › Text Understanding with the Attention Sum Reader Network

Text Understanding with the Attention Sum Reader Network

4 Mar 2016ACL 2016 8arXiv:1603.01547archive 2025-07-28

Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, Jan Kleindienst

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.

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