Due to the significance and value in human-computer interaction and natural language
processing, task-oriented dialog systems are attracting more and more attention in both …

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 …

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 …

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 …

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 …

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 …

Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing
approaches for DST mainly fall into one of two categories, namely, ontology-based and …

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the
preceding conversation. For multi-domain DST, the data sparsity problem is also a major …

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented
dialogue in unseen domains without the expense of collecting in-domain data. In this paper …