Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …

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 …

Abstract Generalized Category Discovery (GCD) aims to recognize both known and novel
categories from a set of unlabeled data, based on another dataset labeled with only known …

New intent discovery aims to uncover novel intent categories from user utterances to expand
the set of supported intent classes. It is a critical task for the development and service …

Large language models (LLMs) are increasingly being used for generating text in a variety
of use cases, including journalistic news articles. Given the potential malicious nature in …

Current NLP models heavily rely on effective representation learning algorithms. Contrastive
learning is one such technique to learn an embedding space such that similar data sample …

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

Data augmentation is a widely used technique to address the problem of text classification
when there is a limited amount of training data. Recent work often tackles this problem using …

We present NLU++, a novel dataset for natural language understanding (NLU) in task-
oriented dialogue (ToD) systems, with the aim to provide a much more challenging …

Transformer-based language models have achieved significant success in various domains.
However, the data-intensive nature of the transformer architecture requires much labeled …