Papers with Compositional generalization
Simple and effective data augmentation for compositional generalization (2024.naacl-long)
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| Challenge: | Compositional generalization is the ability of a system to correctly predict the meaning of complex sentences when trained on simpler sentences. |
| Approach: | They propose to use data augmentation methods to generate additional training data by sampling from an augmentation distribution to generalize to the out-of-distribution test data. |
| Outcome: | The proposed method outperforms existing methods that sampled from the training distribution and outperformed existing methods. |
SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation (2024.acl-long)
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| Challenge: | Existing studies on compositional generalization in data-to-text generation focus on one manifestation, Systematicity, Productivity, Order invariance, and Rule learnability. |
| Approach: | They propose a method for evaluation of compositional generalization in data-to-text generation that includes four aspects of manifestations and allows high-quality evaluation without additional manual annotations. |
| Outcome: | The proposed method is based on two datasets and evaluates existing language models including LLMs. |
Revisiting the Compositional Generalization Abilities of Neural Sequence Models (2022.acl-short)
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| Challenge: | Existing studies have suggested that standard seq-to-seq models lack the ability to generalize compositionally. |
| Approach: | They propose to use one-shot primitive generalization as introduced by the popular SCAN benchmark to modify the training distribution in simple and intuitive ways to achieve near-perfect generalization performance. |
| Outcome: | The proposed model achieves near-perfect generalization performance despite a lack of training data . |
Improving Compositional Generalization in Classification Tasks via Structure Annotations (2021.acl-short)
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| Challenge: | Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. |
| Approach: | They propose to convert a natural language sequence-to-sequence dataset into a classification dataset that requires compositional generalization. |
| Outcome: | The proposed model can generalize compositionally by providing hints on the structure of the input. |
On Evaluating Multilingual Compositional Generalization with Translated Datasets (2023.acl-long)
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| Challenge: | a growing amount of research investigating compositional generalization in NLP is done on English . a critical semantic distortion is a limitation of the translation of datasets . |
| Approach: | They propose to translate a dataset for evaluating compositional generalization in semantic parsing. |
| Outcome: | The proposed benchmarks show that the translation of the MCWQ dataset suffers from semantic distortion. |
Learning Algebraic Recombination for Compositional Generalization (2021.findings-acl)
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Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang
| Challenge: | Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. |
| Approach: | They propose an end-to-end neural model to learn algebraic recombination for compositional generalization. |
| Outcome: | The proposed model is based on two realistic and comprehensive compositional generalization benchmarks. |
Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning (2023.findings-acl)
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| Challenge: | Existing approaches to compositional generalization have been designed with semantic parsing in mind. |
| Approach: | They propose a disentangled sequence-to-sequence model which encourages more disentanglement and improves its compute and memory efficiency. |
| Outcome: | The proposed model improves generalization performance across existing tasks and datasets and a new machine translation benchmark. |
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation (2024.acl-long)
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| Challenge: | Existing MCTG methods face a noticeable performance drop in compositional testing. |
| Approach: | They propose a benchmark to evaluate compositional generalization of MCTG methods by combining multi-aspect labeled datasets and a crafted three-dimensional evaluation protocol. |
| Outcome: | The proposed framework improves compositional generalization performance by 3.64% and 94.4% in compositional testing. |
MC2: A Minimum-Coverage and Dataset-Agnostic Framework for Compositional Generalization of LLMs on Semantic Parsing (2025.findings-emnlp)
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| Challenge: | Existing research relies on dataset-specific designs or a large number of samples to improve compositional generalization of large language models (LLMs) . |
| Approach: | They propose a minimum-coverage framework that can help LLMs achieve compositional generalization by selecting and organizing samples that satisfy the primitive coverage. |
| Outcome: | The proposed framework can improve compositional generalization on different parsing datasets in the minimum-coverage setting. |
Compositional Generalization for Primitive Substitutions (D19-1)
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| Challenge: | Existing approaches to encoding compositional generalization are lacking . et al., 2017) argue that neural networks lack compositional ability . |
| Approach: | They propose a method to encode compositionality in neural networks using two representations . they reduce the entropy in each representation to improve generalization . |
| Outcome: | The proposed approach improves performance on five NLP tasks including instruction learning and machine translation. |
How Do In-Context Examples Affect Compositional Generalization? (2023.acl-long)
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| Challenge: | In-context learning paradigms that focus on large corpus are limiting compositional generalization performance. |
| Approach: | They propose a test suite to investigate in-context compositional generalization . they propose to use examples that are structurally similar to the test case . |
| Outcome: | The proposed test suite investigates in-context compositional generalization performance . it finds that the performance can be affected by the selection of in-const examples . |
Evaluating Structural Generalization in Neural Machine Translation (2024.findings-acl)
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| Challenge: | Existing studies have focused on compositional generalization with semantic parsing, but it remains unclear to what extent models can translate sentences that require structural generalization. |
| Approach: | They construct a machine translation dataset that measures compositional generalization with control of words and sentence structures. |
| Outcome: | The proposed model struggle more in structural generalization than in compositional generalization. |