LSTM-based Deep Learning Models for Answer Ranking
Zhenzhen Li, Jiuming Huang, Zhongcheng Zhou, Haoyu Zhang, Shoufeng Chang, Zhijie Huang · 2016
The learning problem of ranking arises in many tasks, including the question answering, information retrieval, and movie recommendation. In these tasks, the ordering of the answers, documents or movies returned is a critical aspect of the system. Recently, deep learning approaches have gained a lot of attention from the research community and industry for their ability to automatically learn optimal feature representation for a given task. We aim to solve the answer ranking problem in practical question answering system with deep learning approaches. In this paper, we define a composite representation for questions and answers by combining convolutional neural network (CNN) with bidirectional long short-term memory (biLSTM) models, and learn a similarity function to relate them in a supervised way from the available training data. Considering the limited training data, we propose a hypernym strategy to get more general text pairs and test the effectiveness of different strategies. Experimental results on a public benchmark dataset from TREC demonstrate that our system outperforms previous work which requires syntactic features and some deep learning models.