Weakly-Supervised Neural Response Selection from an Ensemble of Task-Specialised Dialogue Agents

• URL: http://arxiv.org/abs/2005.03066v1
• Date: Wed, 6 May 2020 18:40:26 GMT
• Title: Weakly-Supervised Neural Response Selection from an Ensemble of Task-Specialised Dialogue Agents
• Authors: Asir Saeed, Khai Mai, Pham Minh, Nguyen Tuan Duc, Danushka Bollegala
• Abstract summary: We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents. The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism.
• Score: 11.21333474984984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dialogue engines that incorporate different types of agents to converse with humans are popular. However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem. We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a \emph{Neural Response Selection} method. The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism. Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems.

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