# Structural generalization in COGS: Supertagging is (almost) all you need

arXiv: https://arxiv.org/abs/2310.14124 · HTML: https://mlsanity.com/paper/2310.14124/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 21 Oct 2023.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [Measuring Compositional Generalization: A Comprehensive Method on Realistic Data](https://mlsanity.com/paper/1912.09713/) (2019)
- [COGS: A Compositional Generalization Challenge Based on Semantic Interpretation](https://mlsanity.com/paper/2010.05465/) (2020)
- [Good-Enough Compositional Data Augmentation](https://mlsanity.com/paper/1904.09545/) (2019)
- [Improving Text-to-SQL Evaluation Methodology](https://mlsanity.com/paper/1806.09029/) (2018)
- [The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers](https://mlsanity.com/paper/2108.12284/) (2021)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Span-based Semantic Parsing for Compositional Generalization](https://mlsanity.com/paper/2009.06040/) (2020)
- [Learning to Recombine and Resample Data for Compositional Generalization](https://mlsanity.com/paper/2010.03706/) (2020)
- [Meta-Learning to Compositionally Generalize](https://mlsanity.com/paper/2106.04252/) (2021)
- [Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations](https://mlsanity.com/paper/2104.07478/) (2021)
- [Simpler but More Accurate Semantic Dependency Parsing](https://mlsanity.com/paper/1807.01396/) (2018)
- [Improving Compositional Generalization with Latent Structure and Data Augmentation](https://mlsanity.com/paper/2112.07610/) (2021)
- [AMR Parsing as Graph Prediction with Latent Alignment](https://mlsanity.com/paper/1805.05286/) (2018)
- [Compositional Generalization via Semantic Tagging](https://mlsanity.com/paper/2010.11818/) (2020)
- [Learning Algebraic Recombination for Compositional Generalization](https://mlsanity.com/paper/2107.06516/) (2021)
- [AMR Dependency Parsing with a Typed Semantic Algebra](https://mlsanity.com/paper/1805.11465/) (2018)
- [SUBS: Subtree Substitution for Compositional Semantic Parsing](https://mlsanity.com/paper/2205.01538/) (2022)
- [Transcoding compositionally: using attention to find more generalizable solutions](https://mlsanity.com/paper/1906.01234/) (2019)
- [Dependency Parsing as Head Selection](https://mlsanity.com/paper/1606.01280/) (2016)
- [LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing](https://mlsanity.com/paper/2110.07572/) (2021)
- [Compositional Generalisation with Structured Reordering and Fertility Layers](https://mlsanity.com/paper/2210.03183/) (2022)
- [On the inconsistency of separable losses for structured prediction](https://mlsanity.com/paper/2301.10810/) (2023)
- [On graph-based reentrancy-free semantic parsing](https://mlsanity.com/paper/2302.07679/) (2023)

## Built on by (most cited first, 4 of 4)

- [Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations](https://mlsanity.com/paper/2407.04543/) (2024)
- [Representational Homomorphism Predicts and Improves Compositional Generalization In Transformer Language Model](https://mlsanity.com/paper/2601.18858/) (2026)
- [Structural Generalization on SLOG without Hand-Written Rules](https://mlsanity.com/paper/2604.26157/) (2026)
- [On the Role of Directionality in Structural Generalization](https://mlsanity.com/paper/2607.02307/) (2026)

## Abstract (arXiv)

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 semantic parsing framework in several ways to alleviate this issue. Notably, we propose: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) a 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, our results confirm that structural constraints are important for generalization in semantic parsing.