Key-Value Retrieval Networks for Task-Oriented Dialogue
Mihail Eric, Lakshmi Krishnan, Francois Charette, Christopher D. Manning · 2017
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 rulebased system and other existing neural dialogue architectures on the provided domains according to both automatic and human evaluation metrics.