# Semi-interactive Attention Network for Answer Understanding in Reverse-QA

## TL;DR

This paper introduces a semi-interactive attention network model for understanding human answers in reverse-QA scenarios, where machines proactively ask questions and interpret human responses.

## Contribution

The paper proposes the Semi-IAN model for answer understanding in reverse-QA, along with two Chinese datasets, improving upon traditional models in this new QA setting.

## Key findings

- Semi-IAN outperforms conventional models in answer classification accuracy.
- Two Chinese datasets for reverse-QA are compiled and used for evaluation.
- Experimental results demonstrate the effectiveness of the proposed model.

## Abstract

Question answering (QA) is an important natural language processing (NLP) task and has received much attention in academic research and industry communities. Existing QA studies assume that questions are raised by humans and answers are generated by machines. Nevertheless, in many real applications, machines are also required to determine human needs or perceive human states. In such scenarios, machines may proactively raise questions and humans supply answers. Subsequently, machines should attempt to understand the true meaning of these answers. This new QA approach is called reverse-QA (rQA) throughout this paper. In this work, the human answer understanding problem is investigated and solved by classifying the answers into predefined answer-label categories (e.g., True, False, Uncertain). To explore the relationships between questions and answers, we use the interactive attention network (IAN) model and propose an improved structure called semi-interactive attention network (Semi-IAN). Two Chinese data sets for rQA are compiled. We evaluate several conventional text classification models for comparison, and experimental results indicate the promising performance of our proposed models.