Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding

Understand

Generalized text representations are the foundation of many natural language understanding tasks.

• To fully utilize the different corpus, it is inevitable that models need to understand the relevance among them.
• However, many methods ignore the relevance and adopt a single-channel model (a coarse paradigm) directly for all tasks, which lacks enough rationality and interpretation.
• In addition, some existing works learn downstream tasks by stitches skill block(a fine paradigm), which might cause irrationalresults due to its redundancy and noise.