142 research outputs found
Sequential Attention: A Context-Aware Alignment Function for Machine Reading
In this paper we propose a neural network model with a novel Sequential
Attention layer that extends soft attention by assigning weights to words in an
input sequence in a way that takes into account not just how well that word
matches a query, but how well surrounding words match. We evaluate this
approach on the task of reading comprehension (on the Who did What and CNN
datasets) and show that it dramatically improves a strong baseline--the
Stanford Reader--and is competitive with the state of the art.Comment: To appear in ACL 2017 2nd Workshop on Representation Learning for
NLP. Contains additional experiments in section 4 and a revised Figure
Who did What: A Large-Scale Person-Centered Cloze Dataset
We have constructed a new "Who-did-What" dataset of over 200,000
fill-in-the-gap (cloze) multiple choice reading comprehension problems
constructed from the LDC English Gigaword newswire corpus. The WDW dataset has
a variety of novel features. First, in contrast with the CNN and Daily Mail
datasets (Hermann et al., 2015) we avoid using article summaries for question
formation. Instead, each problem is formed from two independent articles --- an
article given as the passage to be read and a separate article on the same
events used to form the question. Second, we avoid anonymization --- each
choice is a person named entity. Third, the problems have been filtered to
remove a fraction that are easily solved by simple baselines, while remaining
84% solvable by humans. We report performance benchmarks of standard systems
and propose the WDW dataset as a challenge task for the community.Comment: To appear at EMNLP 2016. Our dataset is available at
tticnlp.github.io/who_did_wha
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