Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

In the last few years, a large number of automatic evaluation metrics have been proposed for
evaluating Natural Language Generation (NLG) systems. The rapid development and …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

There has been growing interest in parameter-efficient methods to apply pre-trained
language models to downstream tasks. Building on the Prompt Tuning approach of Lester et …

Despite the recent success of multi-task learning and transfer learning for natural language
processing (NLP), few works have systematically studied the effect of scaling up the number …

Extracting entities and relations from unstructured text has attracted increasing attention in
recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping …

Extracting relational triples from unstructured text is crucial for large-scale knowledge graph
construction. However, few existing works excel in solving the overlapping triple problem …

Joint entity and relation extraction is an essential task in natural language processing and
knowledge graph construction. Existing approaches usually decompose the joint extraction …