Papers by Jinshan Zeng
PromptARA: Improving Deep Representation in Hybrid Automatic Readability Assessment with Prompt and Orthogonal Projection (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Readability assessment aims to automatically classify texts based on readers’ reading levels. |
| Approach: | They propose a hybrid automatic readability assessment model using prompts to improve deep feature representations and an orthogonal projection layer to fuse both deep and linguistic features. |
| Outcome: | The proposed model outperforms state-of-the-art models over four English and two Chinese corpora and demonstrates that it is more efficient than existing models. |
Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models do not exploit ordinal nature of difficulty grades and make little effort for initialization to facilitate fine-tuning. |
| Approach: | They propose a readability assessment task that assigns a difficulty grade to a text . they use ordinal regression and pairwise relative text difficulty to train the model . |
| Outcome: | The proposed model outperforms competitive neural models and statistical classifiers on most datasets. |