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 …

Continual learning in task-oriented dialogue systems can allow us to add new domains and
functionalities through time without incurring the high cost of a whole system retraining. In …

Recently, pre-training methods have shown remarkable success in task-oriented dialog
(TOD) systems. However, most existing pre-trained models for TOD focus on either dialog …

Natural language processing (NLP) has a significant impact on society via technologies
such as machine translation and search engines. Despite its success, NLP technology is …

NLP research is impeded by a lack of resources and awareness of the challenges presented
by underrepresented languages and dialects. Focusing on the languages spoken in …

Learning to converse using only a few examples is a great challenge in conversational AI.
The current best conversational models, which are either good chit-chatters (eg, BlenderBot) …

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 …

Pre-training methods with contrastive learning objectives have shown remarkable success
in dialog understanding tasks. However, current contrastive learning solely considers the …