Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries,
which is a core component in a task-oriented dialog system. With the burst of deep neural …

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 …

Cross-domain named entity recognition (NER) models are able to cope with the scarcity
issue of NER samples in target domains. However, most of the existing NER benchmarks …

In recent years, fostered by deep learning technologies and by the high demand for
conversational AI, various approaches have been proposed that address the capacity to …

State-of-the-art abstractive summarization models generally rely on extensive labeled data,
which lowers their generalization ability on domains where such data are not available. In …

Named entity recognition (NER) models generally perform poorly when large training
datasets are unavailable for low-resource domains. Recently, pre-training a large-scale …

Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the
dialog history, DM predicts the dialog state and decides the next action that the dialog agent …

The objective of few-shot named entity recognition is to identify named entities with limited
labeled instances. Previous works have primarily focused on optimizing the traditional token …

Task-oriented dialogue systems are either modularized with separate dialogue state
tracking (DST) and management steps or end-to-end trainable. In either case, the …

Task-oriented dialogue systems use four connected modules, namely, Natural Language
Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural …