A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification

Kumar, Varun; Glaude, Hadrien; Lichy, Cyprien de; Campbell, William M.

doi:10.18653/v1/d19-6101

Cited by 67 publications

(60 citation statements)

References 25 publications

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“…Furthermore, by manipulating the vector representation of data within a learned feature space, a dataset can be augmented in a number of ways, DeVries and Taylor [147] discussed adding noise, interpolating, and extrapolating as useful forms of feature space augmentation, while Kumar et al [150] studied six feature space DA methods to improve classification, including Upsampling, Random Perturbation, Conditional Variational Autoencoder, Linear Delta, Extrapolation and Delta-Encoder.…”

Section: Data-space Vs Feature-space Augmentationmentioning

confidence: 99%

A survey on data‐efficient algorithms in big data era

Adadi¹

2021

J Big Data

193

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The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or time-consuming. This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with less training data and in particular less human supervision. In light of this debate, this work investigates the issue of algorithms’ data hungriness. First, it surveys the issue from different perspectives. Then, it presents a comprehensive review of existing data-efficient methods and systematizes them into four categories. Specifically, the survey covers solution strategies that handle data-efficiency by (i) using non-supervised algorithms that are, by nature, more data-efficient, by (ii) creating artificially more data, by (iii) transferring knowledge from rich-data domains into poor-data domains, or by (iv) altering data-hungry algorithms to reduce their dependency upon the amount of samples, in a way they can perform well in small samples regime. Each strategy is extensively reviewed and discussed. In addition, the emphasis is put on how the four strategies interplay with each other in order to motivate exploration of more robust and data-efficient algorithms. Finally, the survey delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning.

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Section: Data-space Vs Feature-space Augmentationmentioning

confidence: 99%

A survey on data‐efficient algorithms in big data era

Adadi¹

2021

J Big Data

193

View full text Add to dashboard Cite

show abstract

“…In the former, which has been the dominant paradigm in NLP, any additional instances that are added to a training set have an actual surface form representation, i.e. the data points correspond to actual words or sentences (Kim et al, 2019;Kumar et al, 2019;Gao et al, 2019;Andreas, 2020;Croce et al, 2020). The latter adds instances that are fully or partly artificial, meaning they do not correspond to any words or sentences.…”

Section: Data Augmentation Strategies For Hypernymy Detectionmentioning

confidence: 99%

Data Augmentation for Hypernymy Detection

Kober

Weeds

Bertolini

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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The automatic detection of hypernymy relationships represents a challenging problem in NLP. The successful application of stateof-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data. We have developed two novel data augmentation techniques which generate new training examples from existing ones. First, we combine the linguistic principles of hypernym transitivity and intersective modifier-noun composition to generate additional pairs of vectors, such as small dogdog or small dog -animal, for which a hypernymy relationship can be assumed. Second, we use generative adversarial networks (GANs) to generate pairs of vectors for which the hypernymy relation can also be assumed. We furthermore present two complementary strategies for extending an existing dataset by leveraging linguistic resources such as Word-Net. Using an evaluation across 3 different datasets for hypernymy detection and 2 different vector spaces, we demonstrate that both of the proposed automatic data augmentation and dataset extension strategies substantially improve classifier performance.

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“…Another example task which motivates how our framework could be deployed (besides the aforementioned directory example) is Intent Classification in Task-based Dialog (Chen et al, 2019;Kumar et al, 2019;Schuster et al, 2019;Gangal et al, 2020). This is often seen as a hierarchical classification problem (Gupta et al, 2018), with domains (e.g.…”

Section: Another Example Applicationmentioning

confidence: 99%

Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

Mekala

Gangal

Shang

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Existing text classification methods mainly focus on a fixed label set, whereas many realworld applications require extending to new fine-grained classes as the number of samples per label increases. To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pretrained generative language models into the iterative weak supervision strategy. Specifically, we first propose a label-conditioned finetuning formulation to attune these generators for our task. Furthermore, we devise a regularization objective based on the coarse-fine label constraints derived from our problem setting, giving us even further improvements over the prior formulation. Our framework uses the fine-tuned generative models to sample pseudo-training data for training the classifier, and bootstraps on real unlabeled data for model refinement. Extensive experiments and case studies on two real-world datasets demonstrate superior performance over SOTA zeroshot classification baselines.

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A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification

Cited by 67 publications

References 25 publications

A survey on data‐efficient algorithms in big data era

A survey on data‐efficient algorithms in big data era

Data Augmentation for Hypernymy Detection

Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

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