Papers with incremental early-stopping training strategy
Structural generalization in COGS: Supertagging is (almost) all you need (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that neural networks fail to generalize on out-of-distribution examples. |
| Approach: | They extend a neural graph-based parsing framework to address compositional generalization limitations . they introduce a supertagging step with valency constraints and reduce the graph prediction problem . |
| Outcome: | The proposed approach improves results on COGS datasets that require structural generalization. |