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 …

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 …

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 …

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 …

Lifelong event detection aims to incrementally update a model with new event types and
data while retaining the capability on previously learned old types. One critical challenge is …

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 …

Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models
(PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are …

Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft
prompts learned from data, has demonstrated impressive performance on a wide range of …