Task generalization has been a long standing challenge in Natural Language
Processing (NLP). Recent research attempts to improve the task generalization
ability of pre-trained language models by mapping NLP tasks into human-readable
prompted forms. However, these approaches require laborious and inflexible
manual collection of prompts, and different prompts on the same downstream task
may receive unstable performance. We propose Unified Schema Prompt, a flexible
and extensible prompting method, which automatically customizes the learnable
prompts for each task according to the task input schema. It models the shared
knowledge between tasks, while keeping the characteristics of different task
schema, and thus enhances task generalization ability. The schema prompt takes
the explicit data structure of each task to formulate prompts so that little
human effort is involved. To test the task generalization ability of schema
prompt at scale, we conduct schema prompt-based multitask pre-training on a
wide variety of general NLP tasks. The framework achieves strong zero-shot and
few-shot generalization performance on 16 unseen downstream tasks from 8 task
types (e.g., QA, NLI, etc). Furthermore, comprehensive analyses demonstrate the
effectiveness of each component in the schema prompt, its flexibility in task
compositionality, and its ability to improve performance under a full-data
fine-tuning setting.

该研究提出了一种名为 Unified Schema Prompt 的灵活可扩展的提示方法，通过利用每个任务的输入结构来自动自定义学习提示，以增强任务的泛化能力，并在一系列 NLP 任务中进行了实验验证。