Question paraphrasing aims to restate a given question with different expressions but keep the original meaning. Recent approaches are mostly based on neural networks following a sequence-to-sequence fashion, however, these models tend to generate unpredictable results. To overcome this drawback, we propose a pipeline model based on templates. It follows three steps, a) identifies template from the input question, b) retrieves candidate templates, c) fills candidate templates with original topic words. Experiment results on two self-constructed datasets show that our model outperforms the sequence-to-sequence model in a large margin and the advantage is more promising when the size of training sample is small.

In this paper, we propose to incorporate topic aspects information for online comments convincingness evaluation. Our model makes use of graph convolutional network to utilize implicit topic information within a discussion thread to assist the evaluation of convincingness of each single comment. In order to test the effectiveness of our proposed model, we annotate topic information on top of a public dataset for argument convincingness evaluation. Experimental results show that topic information is able to improve the performance for convincingness evaluation. We also make a move to detect topic aspects automatically.