Deep Human Answer Understanding for Natural Reverse QA

TL;DR

This paper introduces AntNet, a novel deep learning model for understanding human answers in reverse question answering, improving interaction naturalness and user experience in human-machine communication.

Contribution

The study proposes AntNet with three innovative modules for answer understanding in reverse QA and creates a new dataset for this task.

Findings

AntNet outperforms existing methods significantly.

The three modules effectively enhance answer understanding.

A new corpus for reverse QA answer understanding is developed.

Abstract

This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers. This procedure exists in many real human-machine interaction applications. However, a crucial problem in human-machine interaction is answer understanding. The existing solutions have relied on mandatory option term selection to avoid automatic answer understanding. However, these solutions have led to unnatural human-computer interaction and negatively affected user experience. To this end, the current study proposes a novel deep answer understanding network, called AntNet, for reverse QA. The network consists of three new modules, namely, skeleton attention for questions, relevance-aware representation of answers, and multi-hop based fusion. As answer understanding for reverse QA has not been explored, a new data corpus is compiled in this study. Experimental results indicate that our proposed network is significantly better than existing methods and those modified from classical natural language processing deep models. The effectiveness of the three new modules is also verified.