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首页> 外文会议>International joint conference on natural language processing;Conference on empirical methods in natural language processing >Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
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Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots

机译：双向交互式匹配网络，用于基于检索的聊天机器人中的个性化响应选择

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摘要

This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the interactive matching network (IMN) which models the matching degree between a context composed of multiple utterances and a response candidate. Compared with previous persona fusion approaches which enhance the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates. Experimental results on PERSONA-CHAT dataset show that the DIM model outperforms its baseline model, i.e.. IMN with persona fusion, by a margin of 14.5% and outperforms the current state-of-the-art model by a margin of 27.7% in terms of top-1 accuracy hits@1.
机译：本文提出了一种双重交互式匹配网络（DIM），用于呈现基于检索的聊天机器人中对话代理的个性。该模型是从交互式匹配网络（IMN）演变而来的，该模型对由多个话语组成的上下文和响应候选者之间的匹配度进行建模。与以前的角色融合方法相比，该方法通过计算与给定角色的相似度来增强上下文的表示，而DIM模型采用双重匹配架构，该架构对响应和上下文之间以及响应和角色之间分别进行交互式匹配，以对候选响应进行排名。在PERSONA-CHAT数据集上的实验结果表明，DIM模型的表现优于其基线模型（即具有角色融合功能的IMN）14.5％，比当前的最新模型高27.7％。前1个命中率的精确度@ 1

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