Collecting and annotating task-oriented dialogues is time-consuming and costly; thus, zero
and few shot learning could greatly benefit dialogue state tracking (DST). In this work, we …

While communicating with a user, a task-oriented dialogue system has to track the user's
needs at each turn according to the conversation history. This process called dialogue state …

General-purpose language models have demonstrated impressive capabilities, performing
on par with state-of-the-art approaches on a range of downstream natural language …

A desirable dialog system should be able to continually learn new skills without forgetting
old ones, and thereby adapt to new domains or tasks in its life cycle. However, continually …

Creating high-quality annotated data for task-oriented dialog (ToD) is known to be
notoriously difficult, and the challenges are amplified when the goal is to create equitable …

Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety
of task-oriented dialogue domains without the expense of collecting in-domain data. In this …

Annotating task-oriented dialogues is notorious for the expensive and difficult data collection
process. Few-shot dialogue state tracking (DST) is a realistic solution to this problem. In this …

Task-oriented dialogue (TOD) systems are required to identify key information from
conversations for the completion of given tasks. Such information is conventionally specified …

Building end-to-end task bots and maintaining their integration with new functionalities using
minimal human efforts is a long-standing challenge in dialog research. Recently large …

As the labeling cost for different modules in task-oriented dialog (ToD) systems is high, a
major challenge is to learn different tasks with the least amount of labeled data. Recently …