Papers with augmentation strategies
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering (2021.findings-emnlp)
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| Challenge: | Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks. |
| Approach: | They propose a self-supervised training stage and a contrastive representation learning stage for spoken question answering with auxiliary tasks and augmentation strategies. |
| Outcome: | The proposed model achieves state-of-the-art results on three SQA benchmarks. |
How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
CoUDA: Coherence Evaluation via Unified Data Augmentation (2024.naacl-long)
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| Challenge: | Existing data augmentations for coherence evaluation rely on heuristic rules and lack designing criteria. |
| Approach: | They propose a data augmentation framework that breaks down coherence into global and local aspects and designs augmentation strategies for both aspects. |
| Outcome: | The proposed framework surpasses recent models in scoring and ranking tasks with 233M parameters. |
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability (2021.acl-long)
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| Challenge: | In-domain experts are recruited to reannotate augmented samples and determine to what extent each strategy preserves the original rating. |
| Approach: | They implement 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires. |
| Outcome: | The data augmentation strategies outperform task-agnostic augmentations and automatic augmentation systems perform worst on the MIND-CA corpus. |
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking (2022.findings-acl)
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| Challenge: | augmentation of task-oriented dialogues has followed standard methods for plain-text despite its richly annotated structure. |
| Approach: | They propose an augmentation framework that utilizes belief state annotations to match turns from various dialogues and form new synthetic dialogues in a bottom-up manner. |
| Outcome: | The proposed framework performs better on seen values and more robust to unseen values on n-shot training scenarios. |
Subword-augmented Embedding for Cloze Reading Comprehension (C18-1)
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| Challenge: | Existing models for machine reading comprehension use word and character representations, but character is not the minimal unit. |
| Approach: | They propose to use subword rather than character for word embedding enhancement . they also empirically explore different augmentation strategies on subword-augmented embedded embedders . |
| Outcome: | The proposed model outperforms state-of-the-art models on public datasets. |
Attention Networks for Augmenting Clinical Text with Support Sets for Diagnosis Prediction (2022.coling-1)
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| Challenge: | Clinical language models may suffer from imbalanced vocabulary for describing diseases or symptoms. |
| Approach: | They propose to augment clinical text with potentially complementary diagnostic codes from prior admissions or as they emerge during differential diagnosis to improve the performance. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art PubMedBERT by up 3% points. |
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)
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| Challenge: | Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance. |
| Approach: | They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
| Outcome: | The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
Limited Linguistic Diversity in Embodied AI Datasets (2026.acl-long)
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Selma Liliane Wanna, Agnes Luhtaru, Jonathan Salfity, Ryan Barron, Juston Moore, Cynthia Matuszek, Mitch Pryor
| Challenge: | Language is a key modality in Vision-Language-Action (VLA) models, yet its linguistic characteristics are poorly documented. |
| Approach: | They propose to audit VLA corpora to characterize what kinds of instructions they contain . they quantify instruction language along complementary dimensions including lexical variety, duplication and overlap, semantic similarity, and syntactic complexity. |
| Outcome: | The proposed dataset audit of several widely used VLA datasets shows that many datasets rely on highly repetitive, template-like commands with limited structural variation yielding a narrow distribution of instruction forms. |
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. |
Improving Low-resource Question Answering by Augmenting Question Information (2023.findings-emnlp)
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Andong Chen, Yuan Sun, Xiaobing Zhao, Rosella Galindo Esparza, Kehai Chen, Yang Xiang, Tiejun Zhao, Min Zhang
| Challenge: | Low-resource questions pose a significant challenge within the field of Question-Answering (QA) tasks. |
| Approach: | They propose a method that leverages large models' internal knowledge to enhance the quality of augmented data by Prompt Answer, Question Generation, and Question Filter. |
| Outcome: | The proposed method outperforms existing augmentation strategies on high-resource QA tasks like SQUAD1.1 and TriviaQA. |
Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval (2025.emnlp-main)
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| Challenge: | Existing research does not explore key factors such as optimal augmentation scale and the necessity of using large augmentation models. |
| Approach: | They propose to use LLMs to augment compact dual-encoder models to improve retrieval performance. |
| Outcome: | The proposed approach improves retrieval performance but its benefits diminish beyond a certain scale even with diverse augmentation strategies. |
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models capture factual knowledge across a wide range of domains, but refining their capabilities on previously seen knowledge remains a challenge. |
| Approach: | They propose a synthetic knowledge ingestion method that leverages fine-grained synthesis and interleaved generation to construct high-quality data representations from raw knowledge sources. |
| Outcome: | The proposed method outperforms baseline methods on question-answering tasks spanning finance, biomedicine, and open-generation domains. |
Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training (2026.acl-long)
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Changhao Jiang, Ming Zhang, Yifei Cao, Junjie Ye, Xiaoran Fan, Shihan Dou, Zhiheng Xi, Jiajun Sun, Yi Dong, Yujiong Shen, Jingqi Tong, Baoyu Fan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods to predict performance of large language models are lacking . authors propose a size-dependent mutual information predictor for closed-book question answering accuracy . |
| Approach: | They propose a size-dependent mutual information predictor that integrates knowledge frequency, knowledge specificity, and model size to forecast closed-book question answering accuracy. |
| Outcome: | The proposed method outperforms baseline models and achieves R2 > 0.7 in predicting QA accuracy without additional training. |
MERIT: Multi-Agent Collaboration for Unsupervised Time Series Representation Learning (2025.findings-acl)
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| Challenge: | Existing approaches to time series representation learning are time-consuming and expert-dependent, which are difficult to generalize across different tasks. |
| Approach: | They propose to use large language model agent to guide unsupervised time series representation learning and a framework to integrate three LLM agents to collaboratively generate positive views for time series data. |
| Outcome: | The proposed framework integrates large language model (LLM) agent to guide unsupervised time series representation learning and compares it with state-of-the-art baselines on multiple time series datasets. |
Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction (2024.lrec-main)
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Samyak Jain, Parth Chhabra, Atula Tejaswi Neerkaje, Puneet Mathur, Ramit Sawhney, Shivam Agarwal, Preslav Nakov, Sudheer Chava, Dinesh Manocha
| Challenge: | Recent advances in the Financial AI realm have expanded the scope of data and methods they use, such as textual and audio cues from financial earnings calls, but limitations exist. |
| Approach: | They propose a Saliency-guided Hierarchical Mixup augmentation technique for multimodal financial prediction tasks. |
| Outcome: | The proposed technique outperforms state-of-the-art methods by 3-7% on financial earnings and conference call datasets. |
Is linguistically-motivated data augmentation worth it? (2025.acl-long)
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| Challenge: | Data augmentation is a widely-used technique for addressing data scarcity. |
| Approach: | They compare linguistically-motivated and linguisticly-naive data augmentation strategies for two low-resource languages with different morphological properties. |
| Outcome: | The proposed methods produce synthetic data that follows all linguistic constraints, but they require linguistic expertise and are more difficult to implement. |