Abstract
In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples. In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work, we extend a neural graph-based parsing framework in several ways to alleviate this issue, notably: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) the reduction of the graph prediction problem to the maximum matching problem; (3) the design of an incremental early-stopping training strategy to prevent overfitting. Experimentally, our approach significantly improves results on examples that require structural generalization in the COGS dataset, a known challenging benchmark for compositional generalization. Overall, these results confirm that structural constraints are important for generalization in semantic parsing.- Anthology ID:
- 2023.emnlp-main.69
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1089–1101
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.69
- DOI:
- 10.18653/v1/2023.emnlp-main.69
- Cite (ACL):
- Alban Petit, Caio Corro, and François Yvon. 2023. Structural generalization in COGS: Supertagging is (almost) all you need. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1089–1101, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Structural generalization in COGS: Supertagging is (almost) all you need (Petit et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.emnlp-main.69.pdf