  This paper proposes an utterance-to-utterance interactive matching network
(U2U-IMN) for multi-turn response selection in retrieval-based chatbots.
Different from previous methods following context-to-response matching or
utterance-to-response matching frameworks, this model treats both contexts and
responses as sequences of utterances when calculating the matching degrees
between them. For a context-response pair, the U2U-IMN model first encodes each
utterance separately using recurrent and self-attention layers. Then, a global
and bidirectional interaction between the context and the response is conducted
using the attention mechanism to collect the matching information between them.
The distances between context and response utterances are employed as a prior
component when calculating the attention weights. Finally, sentence-level
aggregation and context-response-level aggregation are executed in turn to
obtain the feature vector for matching degree prediction. Experiments on four
public datasets showed that our proposed method outperformed baseline methods
on all metrics, achieving a new state-of-the-art performance and demonstrating
compatibility across domains for multi-turn response selection.
