Papers by Frédéric Piedboeuf
Effective Data Augmentation for Sentence Classification Using One VAE per Class (2022.coling-1)
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| Challenge: | Variational auto-encoders and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, but they require more complex manipulations to ensure that the generated examples are useful. |
| Approach: | They propose a simple way to use Variational Auto-Encoders (VAE) for data augmentation by training one VAE per class. |
| Outcome: | The proposed method outperforms generative models on binary classification tasks and several dataset sizes on four different tasks. |
EUROPA: A Legal Multilingual Keyphrase Generation Dataset (2024.acl-long)
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| Challenge: | Keyphrases are short phrases that describe a text and have been used for many applications. |
| Approach: | They present a dataset for multilingual keyphrase generation in the legal domain . it is derived from legal judgments from the Court of Justice of the European Union . they run multilingual models on the corpus and analyze the results . |
| Outcome: | The proposed dataset shows that it is better than existing models and can capture larger input context. |
Is ChatGPT the ultimate Data Augmentation Algorithm? (2023.findings-emnlp)
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| Challenge: | Recent research has examined the use of ChatGPT for data augmentation, but only in limited contexts. |
| Approach: | They use ChatGPT to create new data with paraphrasing and zero-shot generation to compare it to seven other algorithms. |
| Outcome: | The proposed model performs exceptionally well on some simpler data, but it does not perform better than the other algorithms. |
On Evaluation Protocols for Data Augmentation in a Limited Data Scenario (2025.coling-main)
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| Challenge: | Textual data augmentation (DA) is a prolific field of study where novel techniques to create artificial data are regularly proposed. |
| Approach: | They propose to use textual data augmentation (DA) to generate new sentences for text classification in a limited data setting. |
| Outcome: | The proposed methods perform better on small data settings and on large datasets, but they are not as effective on large data sets. |