Key-Value Retrieval Networks for Task-Oriented Dialogue

Mihail Eric; Christopher D. Manning

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arXiv · 1705.05414

Key-Value Retrieval Networks for Task-Oriented Dialogue

Mihail Eric·Christopher D. Manning

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Abstract

Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base. In this work, we seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism. The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. We also release a new dataset of 3,031 dialogues that are grounded through underlying knowledge bases and span three distinct tasks in the in-car personal assistant space: calendar scheduling, weather information retrieval, and point-of-interest navigation. Our architecture is simultaneously trained on data from all domains and significantly outperforms a competitive rule-based system and other existing neural dialogue architectures on the provided domains according to both automatic and human evaluation metrics.

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Mihail Eric, Christopher D. Manning. 2017-07-14. Key-Value Retrieval Networks for Task-Oriented Dialogue. https://arxiv.org/abs/1705.05414

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