| 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. |
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Combine to Describe: Evaluating Compositional Generalization in Image Captioning (2022.acl-srw)
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| Challenge: | Recent work on compositionality has focused on the ability to combine simpler concepts to understand & generate arbitrarily more complex conceptual structures. |
| Approach: | They propose to use a set of image captioning models to benchmark their compositional generalization properties. |
| Outcome: | The proposed models do not generalize in terms of systematicity and productivity, but are robust to synonym substitutions. |
Compositional Generalization by Factorizing Alignment and Translation (2020.acl-srw)
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| Challenge: | a crucial property underlying the expressive power of human language is its systematicity. |
| Approach: | They propose to make an analogous separation between alignment and translation in neural machine translation to capture compositional structure. |
| Outcome: | The proposed architecture outperforms existing neural networks on a compositional generalization task without supervision. |
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. |
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding (2021.findings-emnlp)
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| Challenge: | a recent study shows that deep networks can mimic some human language abilities when presented with novel sentences . a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains is critical to building safe and fair robots, says a new study. |
| Approach: | They build a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains. |
| Outcome: | a new network generalizes its language understanding to compositional domains while generalizing its knowledge when prior work does not. |
Defending Compositionality in Emergent Languages (2022.naacl-srw)
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| Challenge: | a recent paper has suggested that compositionality is a key factor in language productivity, but some research has questioned this. |
| Approach: | They argue that compositionality is essential for successful generalization . they run a two-agent communication game to test this hypothesis . |
| Outcome: | The proposed results show that ANNs can generalize well even without compositional behavior . authors argue that the results are incomplete and weak . |
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. |
Improving Compositional Generalization with Self-Training for Data-to-Text Generation (2022.acl-long)
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| Challenge: | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). |
| Approach: | They propose a template-based input representation that greatly improves the model’s generalization capability. |
| Outcome: | The proposed model improves tree accuracy by 46%+ and reduces slot error rates by 73%+ over the strong baselines on SGD and Weather benchmarks. |
Compositionality and Generalization In Emergent Languages (2020.acl-main)
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| Challenge: | a new study examines whether emergent languages possess compositionality . compositionality is a core concept in linguistics, but linguists' definitions assume full knowledge of primitive expressions and their combination rules. |
| Approach: | They propose to use compositionality to combine expressions according to systematic rules to refer to composite concepts. |
| Outcome: | The proposed language has compositionality, but it is not generalized, the authors show . they show that the more compositional a language is, the more easily it will be picked up by new learners . |
Meta-Learning to Compositionally Generalize (2021.acl-long)
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| Challenge: | Existing studies show that neural networks struggle with compositional generalization . prior work asserts that there are fundamental differences between cognitive and connectionist architectures that make compositional globalization unlikely. |
| Approach: | They propose a meta-learning augmented version of supervised learning that optimizes for out-of-distribution generalization. |
| Outcome: | The proposed model improves generalization performance on COGS and SCAN datasets. |