Open-Domain Why-Question Answering with Adversarial Learning to
Encode Answer Texts
Abstract
In this paper, we propose a method for whyquestion answering (why-QA) that uses an adversarial learning framework. Existing whyQA methods retrieve answer passages that
usually consist of several sentences. These
multi-sentence passages contain not only the
reason sought by a why-question and its connection to the why-question, but also redundant and/or unrelated parts. We use our
proposed Adversarial networks for Generating compact-answer Representation (AGR) to
generate from a passage a vector representation of the non-redundant reason sought by
a why-question and exploit the representation for judging whether the passage actually answers the why-question. Through a series of experiments using Japanese why-QA
datasets, we show that these representations
improve the performance of our why-QA neural model as well as that of a BERT-based
why-QA model. We show that they also improve a state-of-the-art distantly supervised
open-domain QA (DS-QA) method on publicly available English datasets, even though
the target task is not a why-QA.