Mr-Fosdick at S em E val-2023 Task 5: Comparing Dataset Expansion Techniques for Non-Transformer and Transformer Models: Improving Model Performance through Data Augmentation (2023)

Abstract: In supervised learning, a significant amount of data is essential. To achieve this, we generated and evaluated datasets based on a provided dataset using transformer and non-transformer models. By utilizing these generated datasets during the training of new models, we attain a higher balanced accuracy during validation compared to using only the original dataset.

## introduction titles of posts are increasingly utilizing exaggerations and other persuasive techniques to entice clicks. such titles like the following are known as clickbait: ## conclusion throughout this project, we have experimented with various methods to retrieve spoilers from clickbait titles and post texts in order to close the curiosity gap. these methods included a non-transformer based random forest, a custom deberta-based model for task 1, and another transformer model for task 2. however, our primary focus was not on the individual models themselves, but on generating datasets to enhance model robustness and improve the accuracy of our results. by utilizing datasets generated from converting existing question-answering datasets, paraphrasing, question generation, and gpt reformulation, we were able to demonstrate significant improvements in the proposed approaches for task 1. unfortunately, for unknown reasons, we did not observe any improvements in task 2 with the generated data. instead, our task 2 results improved through fine-tuning hyperparameters in the transformer-based approach we described. we demonstrated that the transformer approaches are extremely sensitive to even small amounts of newly generated data, whereas the random forest model could handle large amounts of new data with ease while still reaping benefits. furthermore, our uncertainty analysis demonstrated that both more conservative and imaginative approaches to data generation can prove to be effective. we are of the opinion that our results can be further enhanced by integrating more diverse data generation techniques and better post-selection of the generated data, perhaps even through automatic means.