Recently, ChatGPT, a representative large language model (LLM), has gained considerable
attention due to its powerful emergent abilities. Some researchers suggest that LLMs could …

The prompt-based learning paradigm, which bridges the gap between pre-training and fine-
tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot …

Instruction tuning is an emergent paradigm in NLP wherein natural language instructions
are leveraged with language models to induce zero-shot performance on unseen tasks …

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 …

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 …

Dialogue systems are designed to offer human users social support or functional services
through natural language interactions. Traditional conversation research has put significant …

Dialog models can be greatly strengthened through grounding on various external
information, but grounded dialog corpora are usually not naturally accessible. In this work …

Due to the subtleness, implicity, and different possible interpretations perceived by different
people, detecting undesirable content from text is a nuanced difficulty. It is a long-known risk …

Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level
sentiment information. There are many ABSA tasks, and the current dominant paradigm is to …

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 …