Relation Extraction (RE) is a crucial task in Information Extraction, which entails predicting
relationships between entities within a given sentence. However, extending pre-trained RE …

With the increasing capabilities of large language models (LLMs), these high-performance
models have achieved state-of-the-art results on a wide range of natural language …

Most existing slot filling models tend to memorize inherent patterns of entities and
corresponding contexts from training data. However, these models can lead to system failure …

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 …

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 …

In a realistic dialogue system, the input information from users is often subject to various
types of input perturbations, which affects the slot-filling task. Although rule-based data …

The study elucidates the formidable capabilities of generative AI, specifically in the realm of
language agents and machine translation frameworks, which are garnering increasing …

This paper epitomizes a transformative leap in the realm of multilingual communication. By
harnessing the power of machine learning, this abstract concept materializes into a tangible …

This paper delves into the intricate realm of machine learning-driven language models,
elucidating their pivotal role in contemporary AI-driven language generation. This abstract …