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The recent boom of AI has seen the emergence of many human-computerconversation systems such as Google Assistant, Microsoft Cortana, Amazon Echoand Apple Siri. We introduce and formalize the task of predicting questions inconversations, where the goal is to predict the new question that the user willask, given the past conversational context. This task can be modeled as a"sequence matching" problem, where two sequences are given and the aim is tolearn a model that maps any pair of sequences to a matching probability. Neuralmatching models, which adopt deep neural networks to learn sequencerepresentations and matching scores, have attracted immense research interestsof information retrieval and natural language processing communities. In thispaper, we first study neural matching models for the question retrieval taskthat has been widely explored in the literature, whereas the effectiveness ofneural models for this task is relatively unstudied. We further evaluate theneural matching models in the next question prediction task in conversations.We have used the publicly available Quora data and Ubuntu chat logs in ourexperiments. Our evaluations investigate the potential of neural matchingmodels with representation learning for question retrieval and next questionprediction in conversations. Experimental results show that neural matchingmodels perform well for both tasks.

Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation