Hateful memes spread quickly online and harm society, necessitating effective detection methods. Detecting these memes is challenging due to the need for comprehensive reasoning. Though great efforts have been made, existing detection methods ...

Generative large language models (LLMs), such as GPT-4, have demonstrated remarkable performance across a wide range of NLP tasks. The increased number of LLMs’ parameters enhances their generalization capabilities, but it also results in a higher ...

The design and development of text-based knowledge graph completion (KGC) methods leveraging textual entity descriptions are at the forefront of research. These methods involve advanced optimization techniques such as soft prompts and contrastive ...

For low-resource languages like Tibetan, the availability of pre-trained language models (PLMs) is severely limited both in quantity and performance. Therefore, it is crucial to explore the optimization of these limited PLMs. In this paper, ...

This paper presents a study for low-resource language NER via knowledge transfer using large pre-trained language models. The goals of the study are to enhance the performance of the proposed model for low-resource language NER through knowledge ...

In the world of intelligent legal systems, the process of finding relevant case documents related to a specific legal matter is known as legal case retrieval. While Pretrained Language Models (PLMs) have demonstrated impressive performance in ...

Sentiment analysis is the process of examining people's opinions and emotions towards goods, services, organizations, individuals, and other things, through the use of textual data. It involves categorizing text as positive, negative, or neutral ...

BERT, a pre-trained language model entirely based on attention, has proven to be highly performant for natural language understanding tasks. However, pre-trained language models (PLMs) are often computationally expensive and can hardly be ...${}_{\mathrm{}}$${}_{}$

Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more ...

Machine reading comprehension (MRC) is a typical natural language processing (NLP) task and has developed rapidly in the last few years. Various reading comprehension datasets have been built to support MRC studies. However, large-scale and high-...