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. |
| Approach: | They propose a compositional augmentation strategy that enables multi-grained composition of substructures in the whole training set. |
| Outcome: | The proposed strategy outperforms existing strategies on three compositional generalization benchmarks. |
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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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| Challenge: | Generic unstructured neural networks struggle on out-of-distribution compositional generalization. |
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Span-based Semantic Parsing for Compositional Generalization (2021.acl-long)
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| Challenge: | despite success of sequence-to-sequence models, they fail in compositional generalization . a span-based parser that predicts a utterance over spans improves performance . |
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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. |
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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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| 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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Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization (2021.emnlp-main)
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| Challenge: | Recent research shows that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored. |
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| Challenge: | a proposed data augmentation protocol provides a compositional inductive bias in conditional and unconditional sequence models. |
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Substructure Substitution: Structured Data Augmentation for NLP (2021.findings-acl)
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| Challenge: | Existing work focuses on word-level manipulation or global sequence-to-sequence style generation. |
| Approach: | They propose a family of data augmentation methods that generalize prior methods by substituting substructures with others having the same label. |
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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. |
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