  Text classification is one of the most imperative tasks in natural language
processing (NLP). Recent advances with pre-trained language models (PLMs) have
shown remarkable success on this task. However, the satisfying results obtained
by PLMs heavily depend on the large amounts of task-specific labeled data,
which may not be feasible in many application scenarios due to data access and
privacy constraints. The recently-proposed prompt-based fine-tuning paradigm
improves the performance of PLMs for few-shot text classification with
task-specific templates. Yet, it is unclear how the prompting knowledge can be
transferred across tasks, for the purpose of mutual reinforcement. We propose
TransPrompt v2, a novel transferable prompting framework for few-shot learning
across similar or distant text classification tasks. For learning across
similar tasks, we employ a multi-task meta-knowledge acquisition (MMA)
procedure to train a meta-learner that captures the cross-task transferable
knowledge. For learning across distant tasks, we further inject the task type
descriptions into the prompt, and capture the intra-type and inter-type prompt
embeddings among multiple distant tasks. Additionally, two de-biasing
techniques are further designed to make the trained meta-learner more
task-agnostic and unbiased towards any tasks. After that, the meta-learner can
be adapted to each specific task with better parameters initialization.
Extensive experiments show that TransPrompt v2 outperforms single-task and
cross-task strong baselines over multiple NLP tasks and datasets. We further
show that the meta-learner can effectively improve the performance of PLMs on
previously unseen tasks. In addition, TransPrompt v2 also outperforms strong
fine-tuning baselines when learning with full training sets.
