  The recent boom of AI has seen the emergence of many human-computer
conversation systems such as Google Assistant, Microsoft 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. This 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 attracted immense research interests
of information retrieval and natural language processing communities. In this
paper, we first study neural matching models for the question retrieval task
that has been widely explored in the literature, whereas the effectiveness 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 Quora 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.
