  We consider the importance of different utterances in the context for
selecting the response usually depends on the current query. In this paper, we
propose the model TripleNet to fully model the task with the triple <context,
query, response> instead of <context, response> in previous works. The heart of
TripleNet is a novel attention mechanism named triple attention to model the
relationships within the triple at four levels. The new mechanism updates the
representation for each element based on the attention with the other two
concurrently and symmetrically. We match the triple <C, Q, R> centered on the
response from char to context level for prediction. Experimental results on two
large-scale multi-turn response selection datasets show that the proposed model
can significantly outperform the state-of-the-art methods. TripleNet source
code is available at https://github.com/wtma/TripleNet
