REVIEW 8 cited by
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.
Forward citations
Cited by 8 Pith papers
-
Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification
A continuous exact reformulation lets precision, recall, and F-beta metrics be optimized with gradient methods, avoiding smooth surrogate losses.
-
inversedMixup: Data Augmentation via Inverting Mixed Embeddings
Mixing BERT embeddings and inverting them into text with LLaMA produces interpretable augmented sentences, improves few-shot classification on some datasets, and exposes 'manifold intrusion' in text Mixup.
-
Dual Enhancement on 3D Vision-Language Perception for Monocular 3D Visual Grounding
Unit-robust text augmentation (3DTE) plus text-guided geometry attention (TGE) lifts monocular 3D visual grounding accuracy on Mono3DRefer, notably in far-range scenes.
-
Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis
The paper evaluates four LLMs as text augmenters and reports GPT-3.5 Turbo as the best, and that combining augmentation with GPT topic labels increases BERTopic's discovered topics from 5 to 20 with zero overlap.
-
Explainable AI: XAI-Guided Context-Aware Data Augmentation
XAI-guided augmentation that replaces the least important words, identified by Integrated Gradients, with back-translated synonyms or paraphrases improves hate speech and sentiment classification accuracy by up to 8 p...
-
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching
A multi-agent simulated teaching pipeline creates BOOST-QA, and fine-tuning on it lifts reported LLM benchmark scores by up to 31 points over the original data.
-
3D Skeleton-Based Action Recognition: A Review
A task-oriented review of skeleton-based action recognition that reorganizes known methods along a data processing pipeline and contains no new experimental result.
-
Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs
Fine-tuned DistilBERT on 45,000 SQuAD examples achieves validation F1 0.6536 and 0.1208 s average inference per question on an Intel i7-1355U CPU.
Discussion (0). Sign in to comment.