| Challenge: | a multi-task view of data augmentation allows for a more robust performance than traditional augmentation. |
| Approach: | They propose a multi-task view of data augmentation where original and augmented samples are weighted substantively during training. |
| Outcome: | The proposed model improves on three benchmark text classification datasets. |
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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach (2021.emnlp-main)
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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. |
| Outcome: | The proposed approach improves on six low-resource translation tasks and the baseline and over DA methods. |
Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
| Approach: | They propose to use augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. |
| Outcome: | The proposed methods improve translation and summarization by 6.9% and 7.5% respectively. |
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding (2022.findings-aacl)
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| Challenge: | Meta-Learning requires a large number of training tasks to learn representations that transfer well to unseen tasks. |
| Approach: | They propose a method which synthesizes new tasks by linearly interpolating existing tasks. |
| Outcome: | The proposed method outperforms baselines and does not degrade performance even when it is high. |
Data Augmentation for Text Generation Without Any Augmented Data (2021.acl-long)
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| Challenge: | Existing methods for data augmentation need to define or choose proper data mapping functions to create augmented samples. |
| Approach: | They propose to use data mapping functions to augment text samples without using specific mapping functions. |
| Outcome: | The proposed approach can approximate or even surpass popular data augmentation methods on two text generation tasks with a convergence rate guarantee. |
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning (2022.findings-acl)
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| Challenge: | Existing task weighting methods assign weights only based on training losses, while ignoring the gap between the training loss and generalization loss. |
| Approach: | They propose a task weighting algorithm which automatically weights the tasks via a learning-to-learn paradigm and a multi-task text classification paradigm. |
| Outcome: | Extensive experiments show that the proposed method outperforms existing methods in multi-task text classification. |
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. |
| Outcome: | The proposed methods significantly improve performance when used to pre-train the model and lead to the state-of-the-art results in the GYAFC benchmark dataset. |
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)
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| Challenge: | Existing methods for text data augmentation are limited to simple tasks and weak baselines. |
| Approach: | They propose a data augmentation method FlipDA that uses a generative model and a classifier to generate label-flipped data. |
| Outcome: | The proposed method improves many tasks while not negatively affecting the others. |
Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)
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| Challenge: | True. True. False |
| Approach: | False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions. |
| Outcome: | False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods. |
Rethinking Data Augmentation in Text-to-text Paradigm (2022.coling-1)
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| Challenge: | Existing approaches to augment training data are limited or marginal, or even diminishing or adverse especially given original training corpus is relatively sufficient or the backbone classifiers are PLM based. |
| Approach: | They propose to integrate text-to-text language models and construct a new two-phase framework for augmentation using two novel schemes. |
| Outcome: | The proposed framework synthesizes new samples benefiting from the knowledge learned from pre-trained language models on two public classification datasets and shows remarkable gains. |
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)
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| Challenge: | Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations. |
| Approach: | They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy . |
| Outcome: | The proposed framework outperforms baseline methods on six benchmark datasets. |