Task-oriented dialogue is often decomposed into three tasks: understanding user input,
deciding actions, and generating a response. While such decomposition might suggest a …

Pre-trained language models have been recently shown to benefit task-oriented dialogue
(TOD) systems. Despite their success, existing methods often formulate this task as a …

MultiWOZ 2.0 (Budzianowski et al., 2018) is a recently released multi-domain dialogue
dataset spanning 7 distinct domains and containing over 10,000 dialogues. Though …

Over-dependence on domain ontology and lack of knowledge sharing across domains are
two practical and yet less studied problems of dialogue state tracking. Existing approaches …

The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and
carrying out an effective conversation under various situations to meet the user goal. The …

Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user's goal
during the course of an interaction. Multi-domain and open-vocabulary settings complicate …

We present a new method, Soloist, that uses transfer learning and machine teaching to build
task bots at scale. We parameterize classical modular task-oriented dialog systems using a …

In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design
process of task-oriented dialogue systems and alleviate the over-dependency on annotated …

In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-
values at every dialog turn. Previous neural approaches have modeled domain-and slot …

Recent works in dialogue state tracking (DST) focus on an open vocabulary-based setting to
resolve scalability and generalization issues of the predefined ontology-based approaches …