This article 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. 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. Index Terms-Dialogue, response selection, interactive matching network, utterance-to-utterance.
REF proposed the utterance-to-utterance interactive matching network (U2U-IMN) which treated both contexts and responses as sequences of utterances when calculating the matching degrees between them.
Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
IEEE/ACM Transactions on Audio, Speech, and Language Processing
IEEE/ACM Transactions on Audio, Speech, and Language Processing
Computer Science