Papers with fine-grained neural-symbolic reasoning
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained models excel at graph semantic parsing with rich annotated data, but generalize poorly to out-of-distribution and long-tail examples. |
| Approach: | They propose a compositionality-aware approach to neural-symbolic inference informed by model confidence to capture different aspects of the graph prediction. |
| Outcome: | The proposed method outperforms state-of-the-art models on an English resource grammar parsing problem on standard in-domain and seven OOD corpora. |