Research on prompting has shown excellent performance with little or even no supervised
training across many tasks. However, prompting for machine translation is still under …

Text data augmentation is an effective strategy for overcoming the challenge of limited
sample sizes in many natural language processing (NLP) tasks. This challenge is especially …

Some recent news recommendation (NR) methods introduce a Pre-trained Language Model
(PLM) to encode news representation by following the vanilla pre-train and fine-tune …

Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on
learned prompt vectors, has emerged as a promising approach for efficiently adapting large …

Transformer-based language models have achieved significant success in various domains.
However, the data-intensive nature of the transformer architecture requires much labeled …

Abstract Implicit Discourse Relation Recognition (IDRR) is to detect and classify relation
sense between two text segments without an explicit connective. Vanilla pre-train and fine …

Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs)
for performing downstream tasks in a parameter-efficient manner. While prompt tuning has …

Multi-modal sentiment analysis (MSA) has become more and more attractive in both
academia and industry. The conventional studies normally require massive labeled data to …

Low-Rank Adaptation (LoRA) has recently gained attention for fine-tuning foundation
models by incorporating trainable low-rank matrices, thereby reducing the number of …

The wide spread of false information has detrimental effects on society, and false information
detection has received wide attention. When new domains appear, the relevant labeled data …