Summary of Evaluating the Effectiveness Of Data Augmentation For Emotion Classification in Low-resource Settings, by Aashish Arora et al.
Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings
by Aashish Arora, Elsbeth Turcan
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the effectiveness of various data augmentation techniques for a multi-label emotion classification task using a low-resource dataset. The goal is to improve the performance of machine learning models by increasing the available training data. The authors evaluate different approaches, including autoencoder-based methods and Back Translation. They find that Back Translation outperforms autoencoder-based approaches and that generating multiple examples per training instance leads to further improvement. Additionally, they discover that Back Translation generates a more diverse set of unigrams and trigrams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers tried to make machine learning models better by adding more data to train them. They used different methods to create fake data that looks like the real thing. One method, called Back Translation, worked best. This means it can help us build better models even when we don’t have much data. |
Keywords
» Artificial intelligence » Autoencoder » Classification » Data augmentation » Machine learning » Translation
