2 resources

  • Christopher Ormerod
    |
    Oct 22nd, 2022
    |
    journalArticle
    Christopher Ormerod
    Oct 22nd, 2022

    We introduce a regression-based framework to explore the dependence that global features have on score predictions from pretrained transformer-based language models used for Automated Essay Scoring (AES). We demonstrate that neural networks use approximations of rubric-relevant global features to determine a score prediction. By considering linear models on the hidden states, we can approximate global features and measure their importance to score predictions. This study uses DeBERTa models...

  • Christopher Ormerod
    |
    Feb 23rd, 2022
    |
    preprint
    Christopher Ormerod
    Feb 23rd, 2022

    We investigate the effectiveness of ensembles of pretrained transformer-based language models on short answer questions using the Kaggle Automated Short Answer Scoring dataset. We fine-tune a collection of popular small, base, and large pretrained transformer-based language models, and train one feature-base model on the dataset with the aim of testing ensembles of these models. We used an early stopping mechanism and hyperparameter optimization in training. We observe that generally that...

Last update from database: 22/10/2025, 22:15 (UTC)
Powered by Zotero and Kerko.