All You Need is Attention: Lightweight Attention-based Data Augmentation for Text Classification (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to augment text classification tasks require extensive dataset training. |
| Approach: | They propose a method that uses attention mechanisms to exchange semantically similar words between sentences to generate a greater diversity of synthetic sentences compared to simpler operations like random insertions. |
| Outcome: | The proposed method consistently outperforms baseline methods across diverse text classification conditions. |
Similar Papers
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. |
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (D19-1)
Copied to clipboard
| Challenge: | Existing data augmentation techniques for text classification are difficult to implement and cost a high amount of money. |
| Approach: | They propose to use four simple but powerful operations to boost performance on text classification tasks to improve synonym replacement, random insertion, random swap, and random deletion. |
| Outcome: | The proposed techniques improve performance on five classification tasks and are particularly useful for smaller datasets. |
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. |
GDA: Grammar-based Data Augmentation for Text Classification using Slot Information (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies suggest data augmentation approaches to resolve the low-resource problem in natural language processing tasks. |
| Approach: | They propose to use slot information to augment sentences using a set of injective relations between a sentence’s semantics and its syntactical structure to augment the dataset. |
| Outcome: | The proposed approach outperforms all other data augmentation methods by 19.38%. |
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. |
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work adopts data augmentation techniques to generate pseudo-annotated sentences . existing methods neither preserve semantic consistency of original sentences nor preserve syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations. |
| Approach: | They propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. |
| Outcome: | The proposed technique can bring 2.0% F1 improvements in three datasets under low-resource setting. |
Targeted Augmentation for Low-Resource Event Extraction (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for low-resource information extraction struggle to strike a balance between weak augmentation and drastic augmentation. |
| Approach: | They propose a data augmentation paradigm that uses back validation and targeted augmentation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
| Outcome: | The proposed paradigm produces augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
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. |
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for data augmentation have not been well explored. |
| Approach: | They propose to use punctuation insertion, modal verbs, and double negation to produce diverse forms of sentences. |
| Outcome: | The proposed methods perform better on diverse datasets with semantic similarity and standard negation. |
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (P18-1)
Copied to clipboard
Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin
| Challenge: | Existing deep learning architectures to model compositionality in text sequences require a large number of parameters and expensive computations. |
| Approach: | They propose two additional pooling strategies over word embeddings for improved interpretability and hierarchical pooling for spatial (n-gram) information within text sequences. |
| Outcome: | The proposed pooling strategies improve interpretability and preserve spatial (n-gram) information within text sequences. |