In this paper, we propose an interactive matching network (IMN) to enhance the representations of contexts and responses at both the word level and sentence level 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 significantly outperforms the baseline models by large margins on all metrics, achieving new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.

本文提出了一种针对多轮回复选择任务的交互匹配网络，通过三个方面的词表示构建、具有注意力机制的分层循环编码器，以及全局多轮上下文和回复之间的双向交互计算，实现了在四个公共数据集上的最佳表现。

基于检索式聊天机器人的多轮响应选择的交互匹配网络