A Novel Hierarchical Convolutional Neural Network for Question Answering over Paragraphs
Suncong Zheng, Hongyun Bao, Jun Zhao, Jie Zhang, Zhenyu Qi, Hongwei Hao · 2015
The question of classical Factoid Question Answering (FQA) task is always in the form of a single sentence. There also exists another kind of FQA task, whose question is a descriptive paragraph, such as quiz bowl question answering. Recently, some works try to automatically answer paragraph questions by applying machine learning methods. However, these methods neglect the correlation information between sentences in a paragraph and do not take full advantage of answer embedding information. In this paper, we propose a novel Hierarchical Convolutional Neural Network, called HCNN-E, to settle the task by considering ordinal information of sentences in paragraph and the information of answer embeddings. The experimental results on two public datasets demonstrate the effectiveness of proposed method, and the proposed method can achieve approximately 10% - 20% improvements, when comparing with the baselines.