期刊
出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-69005-6_24
关键词
Answer Triggering; Question Answering; Hierarchical gated recurrent neural tensor network
资金
- National Key Basic Research Program of China [2014CB340504]
- National Natural Science Foundation of China [61371129, 61572245]
In this paper, we focus on the problem of answer triggering addressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep interactions between the candidate answers and the question. Our result on F value achieves 42.6%, which surpasses the baseline by over 10 %.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据