Papers by Michail Mersinias
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization (2022.lrec-1)
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| Challenge: | In recent years, natural language inference has been an emerging research area . a new data augmentation technique is used to augment pre-trained language models . |
| Approach: | They propose to combine automatic contextual data augmentation with a learning procedure for natural language inference. |
| Outcome: | The proposed method outperforms baseline pre-trained language models on benchmark datasets and adversarial examples. |
For Generated Text, Is NLI-Neutral Text the Best Text? (2023.findings-emnlp)
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| Challenge: | a perfectly informative agent would eschew utterances that are redundant or contradict that which they have already said. |
| Approach: | They propose to use a pre-trained NLI model to assess whether a sentence entails, contradicts, or is neutral to prompt and preceding text. |
| Outcome: | The proposed model is predictive of generation errors made by GPT-3. |
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection (2020.lrec-1)
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| Challenge: | Existing work on fake news detection is limited due to the complex nature of the news . |
| Approach: | They propose a statistical approach for the generation of feature vectors to describe a document . they use class label frequency distance to boost machine learning methods . |
| Outcome: | The proposed method outperforms deep learning methods in large datasets while outperforming traditional methods. |