| Challenge: | Existing methods for data augmentation involve performing mathematical operations over the raw input samples or their latent states representations, but these operations are performed in the Euclidean space, simplifying these representations and resulting in noisy interpolations. |
| Approach: | They propose a model-, data-, and modality-agnostic interpolative data augmentation technique operating in the hyperbolic space that captures the complex geometry of input and hidden state hierarchies better than its contemporaries. |
| Outcome: | The proposed technique outperforms state-of-the-art methods on benchmark and low resource datasets across speech, text, and vision modalities. |
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DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification (2022.coling-1)
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| Challenge: | Existing methods to improve the robustness of text classification models are token-, sentence-, and hiddenlevel augmentation. |
| Approach: | They propose an interpolation-based data augmentation approach called DoubleMix to improve the robustness of text classification models by learning the “shifted” features in hidden space. |
| Outcome: | The proposed approach outperforms several popular methods on six text classification benchmark datasets and visual analysis shows that the model features are highly interpretable. |
DMix: Adaptive Distance-aware Interpolative Mixup (2022.acl-short)
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| Challenge: | Interpolation-based regularisation methods such as Mixup have shown to be effective for various tasks and modalities. |
| Approach: | They propose an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space. |
| Outcome: | The proposed method achieves state-of-the-art on sentence classification over existing methods on 8 benchmark datasets across English, Arabic, Turkish, and Hindi languages while achieving benchmark F1 scores in 3 times less number of iterations. |
Augmenting NLP models using Latent Feature Interpolations (2020.coling-main)
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| Challenge: | Existing data augmentation methods with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution. |
| Approach: | They propose a data augmentation technique that uses embeddings and hidden layer representations to construct virtual examples. |
| Outcome: | The proposed method outperforms existing methods in terms of accuracy and robustness to weight pruning. |
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding (2022.naacl-main)
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| Challenge: | Existing data augmentation methods miss the important characteristic of compositionality, meaning of a complex expression is built from its sub-parts. |
| Approach: | They propose a compositional data augmentation approach for natural language understanding called TreeMix that leverages constituency parsing tree to decompose sentences into constituent sub-structures and the Mixup data enhancing technique to recombine them to generate new sentences. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods on text classification and SCAN. |
Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
A Computational Exploration of Exaggeration (D18-1)
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| Challenge: | a new computational approach to exaggeration detection is needed for non-literal phenomena . a corpus of overstatements (or hyperboles) is used to detect exaggrements . |
| Approach: | They propose a computational approach to detect exaggerated sentences using crowdsourcing data . they build a corpus containing overstatements and then evaluate models trained on HYPO . |
| Outcome: | The proposed approach can detect exaggerated sentences using a crowdsourced dataset. |
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification (2023.acl-long)
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| Challenge: | Existing models only address text classification problem in the euclidean space, which is not optimal . e.g., fear and terrified labels may not be differentiated in such space, harming performance . |
| Approach: | They propose a framework that can integrate hyperbolic embeddings to improve the task . they learn label embeddements in the hyperbolical space and then add them to the framework . |
| Outcome: | The proposed framework improves fine-grained emotion classification on two benchmark datasets with 3% improvement over previous state-of-the-art models. |
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification (2021.acl-long)
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| Challenge: | Recent studies on neural networks with pre-trained weights focus on low-dimensional subspace where the embedding vectors computed from input words are located. |
| Approach: | They propose an approach to find and regularize the remainder of the space, referred to as out-of-manifold, which cannot be accessed through the words. |
| Outcome: | The proposed approach is able to fine-tune the out-of-manifold embedding space on text classification benchmarks. |
CIAug: Equipping Interpolative Augmentation with Curriculum Learning (2022.naacl-main)
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| Challenge: | Current methods for interpolative data augmentation select samples at random, which might make it difficult for the model to generalize better and converge faster. |
| Approach: | They propose a curriculum-based learning method that leverages the relative position of samples in hyperbolic embedding space as a complexity measure to gradually mix up increasingly difficult and diverse samples along training. |
| Outcome: | The proposed method achieves state-of-the-art results over existing methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks. |
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. |
| Approach: | They present a comprehensive survey of data augmentation for NLP by summarizing the literature in a structured manner. |
| Outcome: | The proposed methods are used for popular NLP applications and tasks and highlight current challenges and directions for future research. |