An open domain topic prediction model for answer selection

Abstract

We present an open domain topic prediction model for the answer selection task. Different from previous unsupervised topic modeling methods, we automatically extract high quality and large scale 〈sentence, topic〉 pairs from Wikipedia as labeled data, and train an open domain topic prediction model based on convolutional neural network, which can predict the most possible topics for each given input sentence. To verify the usefulness of our proposed approach, we add the topic prediction model into an end-to-end open domain question answering system and evaluate it on the answer selection task, and improvements are obtained on both WikiQA and QASent datasets.