Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in
our daily routines. It seems that the technology has finally ripened to advance the use of CAs …

This paper presents our task-oriented dialog system UBAR which models task-oriented
dialogs on a dialog session level. Specifically, UBAR is acquired by fine-tuning the large pre …

In this paper, we survey the methods and concepts developed for the evaluation of dialogue
systems. Evaluation, in and of itself, is a crucial part during the development process. Often …

As practitioners increasingly deploy machine learning models in critical domains such as
health care, finance, and policy, it becomes vital to ensure that domain experts function …

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 …

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 …

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

Abstract The World Health Organization (WHO) has highlighted the need to greatly
accelerate the prevention of crime and harassment against women and children, thereby …

Existing goal-oriented dialogue datasets focus mainly on identifying slots and values.
However, customer support interactions in reality often involve agents following multi-step …