GDA: Grammar-based Data Augmentation for Text Classification using Slot Information (2023.findings-emnlp)
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| Challenge: | Recent studies suggest data augmentation approaches to resolve the low-resource problem in natural language processing tasks. |
| Approach: | They propose to use slot information to augment sentences using a set of injective relations between a sentence’s semantics and its syntactical structure to augment the dataset. |
| Outcome: | The proposed approach outperforms all other data augmentation methods by 19.38%. |
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GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks (2023.findings-acl)
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| Challenge: | Existing work adopts data augmentation techniques to generate pseudo-annotated sentences . existing methods neither preserve semantic consistency of original sentences nor preserve syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations. |
| Approach: | They propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. |
| Outcome: | The proposed technique can bring 2.0% F1 improvements in three datasets under low-resource setting. |
AEDA: An Easier Data Augmentation Technique for Text Classification (2021.findings-emnlp)
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| Challenge: | AEDA is an easier data augmentation technique than EDA. |
| Approach: | They propose an augmentation technique that includes only random insertion of punctuation marks into the original text. |
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Grammar-based Data Augmentation for Low-Resource Languages: The Case of Guarani-Spanish Neural Machine Translation (2024.naacl-long)
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Agustín Lucas, Alexis Baladón, Victoria Pardiñas, Marvin Agüero-Torales, Santiago Góngora, Luis Chiruzzo
| Challenge: | Low-resource languages suffer from a vicious circle: data is needed to build tools, but available text is scarce. |
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MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction (2023.findings-emnlp)
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| Challenge: | Various data augmentation strategies have been proposed to improve GEC models . high-quality parallel data for GEC is not as widely available . |
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AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes (2024.eacl-srw)
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| Challenge: | Existing methods for text data augmentation suffer from potential semantic damage due to the discrete nature of sentences. |
| Approach: | They propose to adapt AutoAugment to solve this problem by using softEDA to increase text data. |
| Outcome: | The proposed method can boost existing augmentation methods and enhance cutting-edge pretrained language models. |
All You Need is Attention: Lightweight Attention-based Data Augmentation for Text Classification (2024.findings-emnlp)
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| Challenge: | Existing methods to augment text classification tasks require extensive dataset training. |
| Approach: | They propose a method that uses attention mechanisms to exchange semantically similar words between sentences to generate a greater diversity of synthetic sentences compared to simpler operations like random insertions. |
| Outcome: | The proposed method consistently outperforms baseline methods across diverse text classification conditions. |
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. |
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Parallel Data Augmentation for Formality Style Transfer (2020.acl-main)
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| Challenge: | Formality style transfer is a task of automatically transforming text in one particular formality style into another. |
| Approach: | They propose to augment parallel data with three specific data augmentation methods to improve the model's generalization ability and reduce the overfitting risk. |
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Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)
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| Challenge: | Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks. |
| Approach: | They propose a framework for fine-tuning PLMs using a masked language model and Gaussian noise to augment semantically relevant examples with sufficient diversity. |
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An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |