Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures

Understand

While mainstream machine learning methods are known to have limited ability to compositionally generalize, new architectures and techniques continue to be proposed to address this limitation.

• We investigate state-of-the-art techniques and architectures in order to assess their effectiveness in improving compositional generalization in semantic parsing tasks based on the SCAN and CFQ datasets.
• We show that masked language model (MLM) pre-training rivals SCAN-inspired architectures on primitive holdout splits.
• On a more complex compositional task, we show that pre-training leads to significant improvements in performance vs.