When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation (2022.findings-acl)
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| Challenge: | Existing DA methods naively add a certain number of augmented samples without considering the quality and the added computational cost of these samples. |
| Approach: | They propose a data-augmented DA technique that generates or reweights augmented samples . they say it is faster to train and can be plugged into any DA method . |
| Outcome: | The proposed technique is faster to train and more efficient than existing methods. |
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| Challenge: | Existing approaches to generating additional parallel sentences are aimed at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words. |
| Approach: | They propose to use data augmentation techniques to generate additional parallel sentences by reversing the order of the target sentence to produce unfluent target sentences. |
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Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization (D19-1)
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| Challenge: | Existing studies measure the superiority of DA methods in terms of their performance on a specific test set, but some do not exhibit consistent improvements across translation tasks. |
| Approach: | They propose to evaluate DA methods from two perspectives to determine their generalization ability . they find that DA method's test performance does not exhibit consistent improvements across translation tasks . |
| Outcome: | The proposed methods do not exhibit consistent improvements across translation tasks. |
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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On-the-fly Denoising for Data Augmentation in Natural Language Understanding (2024.findings-eacl)
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| Challenge: | Existing methods to improve data augmentation performance may introduce noisy data that impairs training. |
| Approach: | They propose an on-the-fly denoising technique that learns from soft augmented labels provided by an organic teacher model trained on the cleaner original dataset. |
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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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Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
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Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
On Evaluation Protocols for Data Augmentation in a Limited Data Scenario (2025.coling-main)
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| Challenge: | Textual data augmentation (DA) is a prolific field of study where novel techniques to create artificial data are regularly proposed. |
| Approach: | They propose to use textual data augmentation (DA) to generate new sentences for text classification in a limited data setting. |
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Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMax (2021.findings-acl)
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| Challenge: | Existing kNN-based augmentation techniques blindly incorporate all samples, but MiniMax-kNN uses a subset of augmented samples to maximize KL-divergence between teacher and student models. |
| Approach: | They propose a semi-supervised approach to augmented data augmentation using kNN. |
| Outcome: | The proposed method outperforms existing kNN-based augmentation techniques on several classification tasks and requires fewer augmented examples and less computation to achieve superior performance. |
How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval (2023.findings-emnlp)
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Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
| Challenge: | Existing techniques to improve dense retrieval suffer from effectiveness tradeoffs between supervised and zero-shot retrieval, some argue due to the limited model capacity. |
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| Outcome: | The proposed DR can achieve state-of-the-art in supervised and zero-shot evaluations without increasing model size. |
A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
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Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy
| Challenge: | Data augmentation is a field of research that has been underexplored due to the discrete nature of language data. |
| Approach: | They present a comprehensive survey of data augmentation for NLP by summarizing the literature in a structured manner. |
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