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 …

Few-shot named entity recognition (NER) systems aim at recognizing novel-class named
entities based on only a few labeled examples. In this paper, we present a decomposed …

Building dialogue systems requires a large corpus of annotated dialogues. Such datasets
are usually created via crowdsourcing, which is expensive and time-consuming. In this …

Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, eg,
named entity recognition and slot filling, to generalize on an emerging, resource-scarce …

In text classification tasks, useful information is encoded in the label names. Label semantic
aware systems have leveraged this information for improved text classification performance …

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 …

We consider the task of few-shot intent detection, which involves training a deep learning
model to classify utterances based on their underlying intents using only a small amount of …

Multi-label aspect category detection allows a given review sentence to contain multiple
aspect categories, which is shown to be more practical in sentiment analysis and attracting …

With the recent advancements in conversational artificial intelligence (AI), the practical
applications of chatbots have risen significantly in diverse domains such as healthcare …

This work focuses on in-context data augmentation for intent detection. Having found that
augmentation via in-context prompting of large pre-trained language models (PLMs) alone …