# Compositional Generalization via Semantic Tagging

arXiv: https://arxiv.org/abs/2010.11818 · HTML: https://mlsanity.com/paper/2010.11818/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 30 of 32)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations](https://mlsanity.com/paper/2104.07478/) (2021)
- [Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing](https://mlsanity.com/paper/2205.12253/) (2022)
- [Improving Compositional Generalization with Latent Structure and Data Augmentation](https://mlsanity.com/paper/2112.07610/) (2021)
- [SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models](https://mlsanity.com/paper/2205.07381/) (2022)
- [Disentangled Sequence to Sequence Learning for Compositional Generalization](https://mlsanity.com/paper/2110.04655/) (2021)
- [Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization](https://mlsanity.com/paper/2109.02575/) (2021)
- [ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic Interpretation](https://mlsanity.com/paper/2303.13716/) (2023)
- [Inducing Transformer's Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks](https://mlsanity.com/paper/2109.15256/) (2021)
- [SUBS: Subtree Substitution for Compositional Semantic Parsing](https://mlsanity.com/paper/2205.01538/) (2022)
- [A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices](https://mlsanity.com/paper/2406.08787/) (2024)
- [Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality](https://mlsanity.com/paper/2211.15578/) (2022)
- [LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing](https://mlsanity.com/paper/2110.07572/) (2021)
- [SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation](https://mlsanity.com/paper/2310.00796/) (2023)
- [Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks](https://mlsanity.com/paper/2111.05013/) (2021)
- [Compositional Generalization without Trees using Multiset Tagging and Latent Permutations](https://mlsanity.com/paper/2305.16954/) (2023)
- [Data Factors for Better Compositional Generalization](https://mlsanity.com/paper/2311.04420/) (2023)
- [Structural generalization in COGS: Supertagging is (almost) all you need](https://mlsanity.com/paper/2310.14124/) (2023)
- [Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning](https://mlsanity.com/paper/2212.05982/) (2022)
- [Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization](https://mlsanity.com/paper/2305.12169/) (2023)
- [Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering](https://mlsanity.com/paper/2502.03992/) (2025)
- [On graph-based reentrancy-free semantic parsing](https://mlsanity.com/paper/2302.07679/) (2023)
- [Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing](https://mlsanity.com/paper/2305.04346/) (2023)
- [Layer-wise Representation Fusion for Compositional Generalization](https://mlsanity.com/paper/2307.10799/) (2023)
- [Reinforcement Learning for Compositional Generalization with Outcome-Level Optimization](https://mlsanity.com/paper/2605.04920/) (2026)
- [Semantic Operator Prediction and Applications](https://mlsanity.com/paper/2301.00399/) (2023)
- [Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings](https://mlsanity.com/paper/2402.06492/) (2024)
- [Compositional Generalisation for Explainable Hate Speech Detection](https://mlsanity.com/paper/2506.03916/) (2025)
- [LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing](https://mlsanity.com/paper/2205.09607/) (2022)
- [A Short Survey of Systematic Generalization](https://mlsanity.com/paper/2211.11956/) (2022)

## Abstract (arXiv)

Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i.e., they are unable to systematically generalize to unseen compositions of seen components. Motivated by traditional semantic parsing where compositionality is explicitly accounted for by symbolic grammars, we propose a new decoding framework that preserves the expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing. Specifically, we decompose decoding into two phases where an input utterance is first tagged with semantic symbols representing the meaning of individual words, and then a sequence-to-sequence model is used to predict the final meaning representation conditioning on the utterance and the predicted tag sequence. Experimental results on three semantic parsing datasets show that the proposed approach consistently improves compositional generalization across model architectures, domains, and semantic formalisms.