Unsupervised Candidate Answer Extraction through Differentiable
  Masker-Reconstructor Model
Zhuoer Wang Yicheng Wang Ziwei Zhu James Caverlee
Question generation is a widely used data augmentation approach with
extensive applications, and extracting qualified candidate answers from context
passages is a critical step for most question generation systems. However,
existing methods for candidate answer extraction are reliant on linguistic
rules or annotated data that face the partial annotation issue and challenges
in generalization. To overcome these limitations, we propose a novel
unsupervised candidate answer extraction approach that leverages the inherent
structure of context passages through a Differentiable Masker-Reconstructor
(DMR) Model with the enforcement of self-consistency for picking up salient
information tokens. We curated two datasets with exhaustively-annotated answers
and benchmark a comprehensive set of supervised and unsupervised candidate
answer extraction methods. We demonstrate the effectiveness of the DMR model by
showing its performance is superior among unsupervised methods and comparable
to supervised methods.