Spoken language understanding (SLU) is a core task in task-oriented dialogue systems,
which aims at understanding user's current goal through constructing semantic frames. SLU …

In real dialogue scenarios, as there are unknown input noises in the utterances, existing
supervised slot filling models often perform poorly in practical applications. Even though …

Recently, prompt-based generative frameworks have shown impressive capabilities in
sequence labeling tasks. However, in practical dialogue scenarios, relying solely on …

Zero-shot cross-lingual spoken language understanding (SLU) can promote the
globalization application of dialog systems, which has attracted increasing attention. While …

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the
natural language processing field due to its enormous potential for practical applications …

Abstract Most existing Named Entity Recognition (NER) methods require a large scale of
labeled data and exhibit poor performance in low-resource scenarios. Thus in this paper, we …

Few-shot named entity recognition (NER) has shown remarkable progress in identifying
entities in low-resource domains. However, few-shot NER methods still struggle with out-of …

Few-shot Named Entity Recognition (NER) systems are designed to identify new categories
of entities with a limited number of labeled examples. A major challenge encountered by …

Cross-domain few-shot Relation Extraction (RE) aims to transfer knowledge from a source
domain to a different target domain to address low-resource problems. Previous work …

Although significant progress has been made in Chinese Named Entity Recognition (NER)
methods based on deep learning, their performance often falls short in few-shot scenarios …