Papers with Data Augmentation technique
Data Augmentation for Low-Resource Dialogue Summarization (2022.findings-naacl)
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| Challenge: | DADS generates synthetic examples by replacing sections of text from input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the simulated dialogue. |
| Approach: | They propose a Data Augmentation technique for low-resource Dialogue Summarization that uses pretrained language models to generate diverse alternatives. |
| Outcome: | The proposed method generates synthetic examples from a low-resource dataset . it produces topically diverse examples without introducing additional hallucinations . |
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. |
| Approach: | They propose to use a grammar-based system to generate Spanish text and syntactically transfer it to Guarani to boost its performance. |
| Outcome: | The proposed system outperforms existing models by pretraining models with synthetic text. |