# SUBS: Subtree Substitution for Compositional Semantic Parsing

arXiv: https://arxiv.org/abs/2205.01538 · HTML: https://mlsanity.com/paper/2205.01538/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 18 of 18)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [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)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Compositional generalization through meta sequence-to-sequence learning](https://mlsanity.com/paper/1906.05381/) (2019)
- [TableFormer: Robust Transformer Modeling for Table-Text Encoding](https://mlsanity.com/paper/2203.00274/) (2022)
- [Compositional Generalization via Neural-Symbolic Stack Machines](https://mlsanity.com/paper/2008.06662/) (2020)
- [Span-based Semantic Parsing for Compositional Generalization](https://mlsanity.com/paper/2009.06040/) (2020)
- [Understanding tables with intermediate pre-training](https://mlsanity.com/paper/2010.00571/) (2020)
- [Learning to Recombine and Resample Data for Compositional Generalization](https://mlsanity.com/paper/2010.03706/) (2020)
- [Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks](https://mlsanity.com/paper/1807.07545/) (2018)
- [Data Augmentation via Dependency Tree Morphing for Low-Resource Languages](https://mlsanity.com/paper/1903.09460/) (2019)
- [Compositional Generalization via Semantic Tagging](https://mlsanity.com/paper/2010.11818/) (2020)
- [Substructure Substitution: Structured Data Augmentation for NLP](https://mlsanity.com/paper/2101.00411/) (2021)

## Built on by (most cited first, 16 of 16)

- [Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing](https://mlsanity.com/paper/2205.12253/) (2022)
- [SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models](https://mlsanity.com/paper/2205.07381/) (2022)
- [Learning to Substitute Spans towards Improving Compositional Generalization](https://mlsanity.com/paper/2306.02840/) (2023)
- [Hierarchical Phrase-based Sequence-to-Sequence Learning](https://mlsanity.com/paper/2211.07906/) (2022)
- [Structural generalization in COGS: Supertagging is (almost) all you need](https://mlsanity.com/paper/2310.14124/) (2023)
- [Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations](https://mlsanity.com/paper/2407.04543/) (2024)
- [Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing](https://mlsanity.com/paper/2210.04878/) (2022)
- [On the Security Vulnerabilities of Text-to-SQL Models](https://mlsanity.com/paper/2211.15363/) (2022)
- [Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization](https://mlsanity.com/paper/2305.12169/) (2023)
- [On graph-based reentrancy-free semantic parsing](https://mlsanity.com/paper/2302.07679/) (2023)
- [Layer-wise Representation Fusion for Compositional Generalization](https://mlsanity.com/paper/2307.10799/) (2023)
- [Efficient Model-Agnostic Multi-Group Equivariant Networks](https://mlsanity.com/paper/2310.09675/) (2023)
- [Learning to Substitute Components for Compositional Generalization](https://mlsanity.com/paper/2502.20834/) (2025)
- [A Short Survey of Systematic Generalization](https://mlsanity.com/paper/2211.11956/) (2022)
- [Semantic Parsing in Limited Resource Conditions](https://mlsanity.com/paper/2309.07429/) (2023)
- [Data Distributional Properties As Inductive Bias for Systematic Generalization](https://mlsanity.com/paper/2502.20499/) (2025)

## Abstract (arXiv)

Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. Several data augmentation methods have been proposed to alleviate this problem. However, prior work only leveraged superficial grammar or rules for data augmentation, which resulted in limited improvement. We propose to use subtree substitution for compositional data augmentation, where we consider subtrees with similar semantic functions as exchangeable. Our experiments showed that such augmented data led to significantly better performance on SCAN and GeoQuery, and reached new SOTA on compositional split of GeoQuery.