Semi-interactive Attention Network for Answer Understanding in Reverse-QA | |
Luo G(罗冠) | |
2019 | |
会议日期 | April 14-17, 2019 |
会议地点 | Macau, China |
英文摘要 | 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 mean-ing 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 prede- ned 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 classi cation models for comparison, and experimental results indicate the promising performance of our proposed models. |
会议录出版者 | Springer |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/26111] |
专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
通讯作者 | Luo G(罗冠) |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Luo G. Semi-interactive Attention Network for Answer Understanding in Reverse-QA[C]. 见:. Macau, China. April 14-17, 2019. |
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