Year: 2017
Type: conference-paper
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 rulebased system and other existing neural dialogue architectures on the provided domains according to both automatic and human evaluation metrics.
DOI: 10.18653/v1/w17-5506
Authors: Mihail Eric, Lakshmi Krishnan, Francois Charette, Christopher D. Manning
Institution: Ford Motor Company (United States)
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2616122292