TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots

TL;DR

TripleNet introduces a novel triple attention mechanism that models the relationships among context, query, and response in multi-turn response selection, significantly improving performance over previous methods.

Contribution

The paper proposes TripleNet, a new model with triple attention that fully models the <context, query, response> triple, advancing multi-turn response selection in retrieval-based chatbots.

Findings

Outperforms state-of-the-art methods on large-scale datasets

Effectively models relationships among context, query, and response

Demonstrates significant accuracy improvements

Abstract

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