Text Augmentation Using Dataset Reconstruction for Low-Resource Classification (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for text classification use labeled data, but labeles are expensive and difficult to obtain. |
| Approach: | They propose a novel method of data augmentation using the text-generation capabilities of language models. |
| Outcome: | The proposed method improves the current state-of-the-art methods for data augmentation on multi-class datasets. |
Similar Papers
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020.emnlp-main)
Copied to clipboard
Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao
| Challenge: | Data augmentation techniques are widely used to improve machine learning performance . however, due to the complexity of language, it is difficult to generalize such rules for languages. |
| Approach: | They propose a method to generate high quality synthetic data for low-resource tagging tasks . they use unlabeled data only and unlabelled data plus a knowledge base . |
| Outcome: | The proposed method outperforms baselines on NER, part of speech and target based sentiment analysis tasks. |
Data Augmentation for Text Generation Without Any Augmented Data (2021.acl-long)
Copied to clipboard
| 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. |
Data Augmentation for Cross-Domain Named Entity Recognition (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition focus on augmenting in-domain data in low-resource scenarios where annotated data is limited. |
| Approach: | They propose a neural architecture to transform data from high-resource to low-resourced domains by learning the patterns in the text that differentiate them. |
| Outcome: | The proposed approach improves on high-resource domain representations over high- and low-resourced domains. |
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes (2024.eacl-srw)
Copied to clipboard
| Challenge: | Existing methods for text data augmentation suffer from potential semantic damage due to the discrete nature of sentences. |
| Approach: | They propose to adapt AutoAugment to solve this problem by using softEDA to increase text data. |
| Outcome: | The proposed method can boost existing augmentation methods and enhance cutting-edge pretrained language models. |
Rethinking Data Augmentation in Text-to-text Paradigm (2022.coling-1)
Copied to clipboard
| 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. |
AEDA: An Easier Data Augmentation Technique for Text Classification (2021.findings-emnlp)
Copied to clipboard
| Challenge: | AEDA is an easier data augmentation technique than EDA. |
| Approach: | They propose an augmentation technique that includes only random insertion of punctuation marks into the original text. |
| Outcome: | The proposed method is easier to implement for data augmentation than EDA method. |
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)
Copied to clipboard
| 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. |
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks (2022.acl-short)
Copied to clipboard
| Challenge: | Experimental results show text smoothing outperforms data augmentation methods by a substantial margin. |
| Approach: | They propose to use a masked language model to convert a token to a smoothed representation by converting a sentence from its one-hot representation to 'controllable smoothes' they propose to combine text smoothing with other data augmentation methods to achieve better performance. |
| Outcome: | The proposed method outperforms mainstream data augmentation methods by a substantial margin on different datasets in a low-resource regime. |
A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
Copied to clipboard
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. |
Exploring Data Augmentation in Neural DRS-to-Text Generation (2024.eacl-long)
Copied to clipboard
| Challenge: | Neural networks are notoriously data-hungry, resulting in ungrammatical texts . data augmentation requires a specific design for a structurally rich input format . |
| Approach: | They propose to selectively augment a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. |
| Outcome: | The proposed approach selectively augments a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. |