Learning Label Modular Prompts for Text Classification in the Wild
This addresses the problem of adapting to changing data and tasks in real-world NLP applications, though it is incremental as it builds on existing modular and prompt tuning approaches.
The paper tackles text classification in non-stationary environments by proposing MODULARPROMPT, a label-modular prompt tuning framework that outperforms baselines by a large margin in challenging settings.
Machine learning models usually assume i.i.d data during training and testing, but data and tasks in real world often change over time. To emulate the transient nature of real world, we propose a challenging but practical task: text classification in-the-wild, which introduces different non-stationary training/testing stages. Decomposing a complex task into modular components can enable robust generalisation under such non-stationary environment. However, current modular approaches in NLP do not take advantage of recent advances in parameter efficient tuning of pretrained language models. To close this gap, we propose MODULARPROMPT, a label-modular prompt tuning framework for text classification tasks. In MODULARPROMPT, the input prompt consists of a sequence of soft label prompts, each encoding modular knowledge related to the corresponding class label. In two of most formidable settings, MODULARPROMPT outperforms relevant baselines by a large margin demonstrating strong generalisation ability. We also conduct comprehensive analysis to validate whether the learned prompts satisfy properties of a modular representation.