Unobserved Local Structures Make Compositional Generalization Hard

Ben Bogin, Shivanshu Gupta, Jonathan Berant


Abstract
While recent work has shown that sequence-to-sequence models struggle to generalize to new compositions (termed compositional generalization), little is known on what makes compositional generalization hard on a particular test instance. In this work, we investigate the factors that make generalization to certain test instances challenging. We first substantiate that some examples are more difficult than others by showing that different models consistently fail or succeed on the same test instances. Then, we propose a criterion for the difficulty of an example: a test instance is hard if it contains a local structure that was not observed at training time. We formulate a simple decision rule based on this criterion and empirically show it predicts instance-level generalization well across 5 different semantic parsing datasets, substantially better than alternative decision rules. Last, we show local structures can be leveraged for creating difficult adversarial compositional splits and also to improve compositional generalization under limited training budgets by strategically selecting examples for the training set.
Anthology ID:
2022.emnlp-main.175
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2731–2747
Language:
URL:
https://aclanthology.org/2022.emnlp-main.175
DOI:
10.18653/v1/2022.emnlp-main.175
Bibkey:
Cite (ACL):
Ben Bogin, Shivanshu Gupta, and Jonathan Berant. 2022. Unobserved Local Structures Make Compositional Generalization Hard. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2731–2747, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Unobserved Local Structures Make Compositional Generalization Hard (Bogin et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-main.175.pdf