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. |
| Outcome: | The proposed model improves compositional generalization performance by exploiting alignment annotations between sentences and their corresponding meaning representations. |
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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. |
| 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. |
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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. |
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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. |
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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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Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing (2023.findings-eacl)
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| Challenge: | Existing approaches that model alignments between sentences fail at compositional generalization tasks, resulting in a resurgence of such approaches. |
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LexSym: Compositionality as Lexical Symmetry (2023.acl-long)
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| Challenge: | Existing approaches to generalize compositional models fail to generalise from small datasets. |
| Approach: | They propose a domain-general and model-agnostic formulation of compositionality as a constraint on symmetries of data distributions rather than models. |
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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. |
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Deep Generative Model for Joint Alignment and Word Representation (N18-1)
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| Challenge: | EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. |
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Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention (2021.naacl-main)
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Pengcheng Yin, Hao Fang, Graham Neubig, Adam Pauls, Emmanouil Antonios Platanios, Yu Su, Sam Thomson, Jacob Andreas
| Challenge: | Existing approaches to compositional generalization in semantic parsers focus on word-level alignments, but they focus on spans. |
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| Outcome: | The proposed method improves on three benchmarks of compositional generalization. |
SUBS: Subtree Substitution for Compositional Semantic Parsing (2022.naacl-main)
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| Challenge: | Semantic parsing models fail at compositional generalization due to lack of reasoning ability. |
| Approach: | They propose to use subtree substitution for compositional data augmentation to increase the number of subtreas with similar semantic functions as exchangeable. |
| Outcome: | The proposed method improves performance on Scan and GeoQuery, and new SOTA on compositional split of GeoQuery. |