Learning Recurrent Span Representations for Extractive Question Answering
Abstract
The reading comprehension task, that asks questions about a given evidence document,
is a central problem in natural language understanding. Recent formulations
of this task have typically focused on answer selection from a set of candidates
pre-defined manually or through the use of an external NLP pipeline. However,
Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers
can be arbitrary strings from the supplied text. In this paper, we focus on
this answer extraction task, presenting a novel model architecture that efficiently
builds fixed length representations of all spans in the evidence document with a recurrent
network. We show that scoring explicit span representations significantly
improves performance over other approaches that factor the prediction into separate
predictions about words or start and end markers. Our approach improves
upon the best published results of Wang & Jiang (2016) by 5% and decreases the
error of Rajpurkar et al.’s baseline by > 50%.
is a central problem in natural language understanding. Recent formulations
of this task have typically focused on answer selection from a set of candidates
pre-defined manually or through the use of an external NLP pipeline. However,
Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers
can be arbitrary strings from the supplied text. In this paper, we focus on
this answer extraction task, presenting a novel model architecture that efficiently
builds fixed length representations of all spans in the evidence document with a recurrent
network. We show that scoring explicit span representations significantly
improves performance over other approaches that factor the prediction into separate
predictions about words or start and end markers. Our approach improves
upon the best published results of Wang & Jiang (2016) by 5% and decreases the
error of Rajpurkar et al.’s baseline by > 50%.
Research Areas
