Improving Compositional Generalization with Latent Structure and Data Augmentation (2022.naacl-main)
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| Challenge: | Generic unstructured neural networks struggle on out-of-distribution compositional generalization. |
| Approach: | They propose a method to recombinate examples from a model called Compositional Structure Learner and add them to a pre-trained sequence-to-sequence model. |
| Outcome: | The proposed model is even stronger than a T5-CSL ensemble on two real world compositional generalization tasks. |
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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. |
Learning to Substitute Spans towards Improving Compositional Generalization (2023.acl-long)
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| Challenge: | despite the rising prevalence of neural sequence models, there is a deficiency in compositional generalization. |
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Align and Augment: Generative Data Augmentation for Compositional Generalization (2024.eacl-long)
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| Challenge: | Recent work on semantic parsing has shown that seq2seq models find compositional generalization challenging. |
| Approach: | They propose a data-augmentation strategy that exploits alignment annotations between sentences and their corresponding meaning representations to improve compositional generalization. |
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| Challenge: | Existing neural models lack systematic compositionality in learning symbolic structures . existing models lack this ability in learning symbols, despite being able to understand complex structures. |
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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. |
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Sequence-Level Mixed Sample Data Augmentation (2020.emnlp-main)
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| Challenge: | Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. |
| Approach: | They propose a data augmentation approach to encourage compositional behavior in neural networks . they propose to softly combine input/output sequences from the training set . |
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Compositional Generalization via Semantic Tagging (2021.findings-emnlp)
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| Challenge: | Existing neural sequence-to-sequence models fail at compositional generalization, i.e., they cannot generalize to unseen compositions of seen components. |
| Approach: | They propose a decoding framework that preserves expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing. |
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Good-Enough Compositional Data Augmentation (2020.acl-main)
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| Challenge: | a proposed data augmentation protocol provides a compositional inductive bias in conditional and unconditional sequence models. |
| Approach: | They propose a data augmentation protocol that provides a compositional inductive bias in conditional and unconditional sequence models by replacing discontinuous fragments with other fragments that appear in at least one similar environment. |
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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. |
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? (2021.acl-long)
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| Challenge: | Existing approaches to semantic parsing only evaluated on synthetic datasets that are not representative of natural language variation. |
| Approach: | They propose a semantic parsing approach that handles both natural language variation and compositional generalization. |
| Outcome: | The proposed model outperforms existing models across compositional generalization challenges on non-synthetic datasets while being competitive with the state-of-the-art on standard evaluations. |