Papers with Data Augmentation
Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation (2022.naacl-srw)
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| Challenge: | Cognitive distortions are one of the targets of cognitive behavioral therapy (CBT). |
| Approach: | They propose to use Easy Data Augmentation, back translation, and mixup techniques to detect distortions in text-based therapy messages. |
| Outcome: | The proposed methods improve performance with optimized parameter settings for rare classes with an augmented model, MentalBERT. |
ODDA: An OODA-Driven Diverse Data Augmentation Framework for Low-Resource Relation Extraction (2025.findings-acl)
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| Challenge: | Existing methods for low-resource relation extraction (LRE) lack diversity, leading to suboptimal performance. |
| Approach: | They propose to use large language models to augment relation extraction models by observing the RE model's behavior and replacing schema constraints with attribute constraints. |
| Outcome: | Experiments on three widely-used benchmarks show that the proposed method outperforms state-of-the-art methods while maintaining enhanced model stability. |
How DDAIR you? Disambiguated Data Augmentation for Intent Recognition (2026.eacl-short)
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| Challenge: | Large Language Models (LLMs) produce ambiguous examples with regard to untargeted classes. |
| Approach: | They propose to use a sentence transformer to detect ambiguous augmented examples generated by Large Language Models for intent recognition. |
| Outcome: | The proposed method improves the quality of augmented data generated by large language models in low-resource scenarios. |
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery (2025.findings-acl)
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| Challenge: | Existing statistical causal discovery methods rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships. |
| Approach: | They propose a multi-agent system powered by tool-augmented Large Language Models that can combine data from multiple modalities and integrate multi-modal data for knowledge-driven reasoning. |
| Outcome: | The proposed system has two agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven reasoning. |
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. |
| Outcome: | The proposed method improves on text classification and question-answering tasks on general augmentation techniques and prevents overfitting on noisy labels. |
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. |
DASA-Trans-STM: Adaptive Efficient Transformer for Short Text Matching using Data Augmentation and Semantic Awareness (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shown impressive versatility across various tasks. |
| Approach: | They propose a novel adaptive Transformer for Chinese short text matching using data augmentation and semantic awareness. |
| Outcome: | The proposed model can deal with word ambiguity in Chinese on four available datasets. |
Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting (2023.findings-emnlp)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences. |
| Approach: | They propose a low-resource approach to extract comparative opinion quintuples from comparative sentences . they propose augmentation using ChatGPT and a data-centric approach . |
| Outcome: | The proposed approach improves the existing pipeline-based method and achieves state-of-the-art results. |
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks (2022.acl-long)
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| Challenge: | Existing approaches to build labeled training data from domain-specific data are expensive to obtain. |
| Approach: | They propose a Prompt-based Data Augmentation model which only trains small-scale Soft Promptes in frozen Pre-trained Language Models. |
| Outcome: | The proposed model outperforms several baseline models on four benchmarks and is complementary with unlabeled in-domain data. |
Learning to Generate Examples for Semantic Processing Tasks (2022.naacl-main)
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| Challenge: | Recent data augmentation techniques can help to deal with low resource settings, such as BERT, but they can hurt the results. |
| Approach: | They propose a neural approach to automatically learn to generate new examples using a pre-trained sequence-to-sequence model. |
| Outcome: | The proposed approach outperforms existing methods on text classification and natural language inference tasks by 10%. |
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? (2020.findings-emnlp)
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| Challenge: | Task-agnostic data augmentations have proven widely effective in computer vision, even on pretrained models. |
| Approach: | They examine the effects of two types of task-agnostic data augmentation on pretrained transformers using 5 classification tasks and 6 datasets. |
| Outcome: | The proposed techniques improve performance on 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers. |
GBT: Generative Boosting Training Approach for Paraphrase Identification (2023.findings-emnlp)
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| Challenge: | Paraphrase Identification (PI) is a fundamental natural language understanding task with non-trivial challenges. |
| Approach: | They propose a Generative Boosting Training approach for Paraphrase Identification (PI) they use a seq2seq model to perform DA on misclassified instances periodically . |
| Outcome: | The proposed method outperforms state-of-the-art PI models on English and Chinese PI tasks with good efficiency and effectiveness. |
CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation (2022.coling-1)
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Md Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Phillippe Langlais, Pascal Poupart
| Challenge: | Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained language models. |
| Approach: | They propose a data augmentation technique tailored for knowledge distillation based on contrastive loss to improve masked adversarial data augmented by intermediate layer matching. |
| Outcome: | The proposed technique outperforms state-of-the-art methods on the GLUE benchmark and in an out-of domain evaluation. |
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. |
| Approach: | They propose to use diverse queries and sources of supervision to train a generalizable DR to achieve high accuracy in both supervised and zero-shot retrieval. |
| Outcome: | The proposed DR can achieve state-of-the-art in supervised and zero-shot evaluations without increasing model size. |
It’s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution (D19-1)
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| Challenge: | Existing attempts to mitigate gender bias rely on operationalisation of gender bias as a projection over a linear subspace. |
| Approach: | They propose to operationalise gender bias as a linear subspace and augmented a corpus to remove bias by swapping all inherently-gendered words in the copy. |
| Outcome: | The proposed approach outperforms projection-based methods at the task of drawing non-biased gender analogies by an average of 19% across both corpora. |
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. |
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 . |
| Approach: | They propose a data augmentation approach that strategically augments real data by generating pseudo data. |
| Outcome: | The proposed approach significantly improves GEC models on English and Chinese datasets. |
Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition (2026.findings-acl)
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Xinyu Liu, Kai fu, Yinghan Shi, Quanyou Chu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, Bo Xu
| Challenge: | Existing methods for Named Entity Recognition (NER) ignore the internal state of the target model. |
| Approach: | They propose a framework to repair model-specific errors by using a model-based approach . they employ cross-validation to identify model- specific Hard Data and a memory tree to induce macro-level error patterns from micro-level failures. |
| Outcome: | The proposed framework yields significant performance gains on Twitter and other platforms. |