ABSTRACT e recent boom of AI has seen the emergence of many humancomputer conversation systems such as Google Assistant, Microso Cortana, Amazon Echo and Apple Siri. We introduce and formalize the task of predicting questions in conversations, where the goal is to predict the new question that the user will ask, given the past conversational context. is task can be modeled as a "sequence matching" problem, where two sequences are given and the aim is to learn a model that maps any pair of sequences to a matching probability. Neural matching models, which adopt deep neural networks to learn sequence representations and matching scores, have a racted immense research interests of information retrieval and natural language processing communities. In this paper, we rst study neural matching models for the question retrieval task that has been widely explored in the literature, whereas the e ectiveness of neural models for this task is relatively unstudied. We further evaluate the neural matching models in the next question prediction task in conversations. We have used the publicly available ora data and Ubuntu chat logs in our experiments. Our evaluations investigate the potential of neural matching models with representation learning for question retrieval and next question prediction in conversations. Experimental results show that neural matching models perform well for both tasks.
Yang et al. REF investigated predicting the new question that the user will ask given the past conversational context.
Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation
ArXiv
ArXiv
Computer Science