# Systematic Generalization on gSCAN with Language Conditioned Embedding

arXiv: https://arxiv.org/abs/2009.05552 · HTML: https://mlsanity.com/paper/2009.05552/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 15 of 15)

- [Position: Understanding LLMs Requires More Than Statistical Generalization](https://mlsanity.com/paper/2405.01964/) (2024)
- [ReaSCAN: Compositional Reasoning in Language Grounding](https://mlsanity.com/paper/2109.08994/) (2021)
- [Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?](https://mlsanity.com/paper/2109.12243/) (2021)
- [A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices](https://mlsanity.com/paper/2406.08787/) (2024)
- [Improving Systematic Generalization Through Modularity and Augmentation](https://mlsanity.com/paper/2202.10745/) (2022)
- [When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks](https://mlsanity.com/paper/2210.12786/) (2022)
- [Can LLM find the green circle? Investigation and Human-guided tool manipulation for compositional generalization](https://mlsanity.com/paper/2312.07763/) (2023)
- [Compositional Generalization in Grounded Language Learning via Induced Model Sparsity](https://mlsanity.com/paper/2207.02518/) (2022)
- [Recursive Decoding: A Situated Cognition Approach to Compositional Generation in Grounded Language Understanding](https://mlsanity.com/paper/2201.11766/) (2022)
- [Trust in Language Grounding: a new AI challenge for human-robot teams](https://mlsanity.com/paper/2209.02066/) (2022)
- [Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments](https://mlsanity.com/paper/2311.04364/) (2023)
- [Compositional Generalization in Spoken Language Understanding](https://mlsanity.com/paper/2312.15815/) (2023)
- [A Short Survey of Systematic Generalization](https://mlsanity.com/paper/2211.11956/) (2022)
- [Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory](https://mlsanity.com/paper/2404.11870/) (2024)
- [Data Distributional Properties As Inductive Bias for Systematic Generalization](https://mlsanity.com/paper/2502.20499/) (2025)

## Abstract (arXiv)

Systematic Generalization refers to a learning algorithm's ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the test set is systematically different from the training data. We hypothesize that explicitly modeling the relations between objects in their contexts while learning their representations will help achieve systematic generalization. Therefore, we propose a novel method that learns objects' contextualized embeddings with dynamic message passing conditioned on the input natural language and end-to-end trainable with other downstream deep learning modules. To our knowledge, this model is the first one that significantly outperforms the provided baseline and reaches state-of-the-art performance on grounded-SCAN (gSCAN), a grounded natural language navigation dataset designed to require systematic generalization in its test splits.