  In this paper, we propose an interactive matching network (IMN) for the
multi-turn response selection task. First, IMN constructs word representations
from three aspects to address the challenge of out-of-vocabulary (OOV) words.
Second, an attentive hierarchical recurrent encoder (AHRE), which is capable of
encoding sentences hierarchically and generating more descriptive
representations by aggregating with an attention mechanism, is designed.
Finally, the bidirectional interactions between whole multi-turn contexts and
response candidates are calculated to derive the matching information between
them. Experiments on four public datasets show that IMN outperforms the
baseline models on all metrics, achieving a new state-of-the-art performance
and demonstrating compatibility across domains for multi-turn response
selection.
