Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts
Jong–Hoon Oh, Kazuma Kadowaki, Julien Kloetzer, Ryu Iida, Kentaro Torisawa · 2019
In this paper, we propose a method for whyquestion answering (why-QA) that uses an adversarial learning framework.Existing why-QA 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.