Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay Scoring
Table 5: QWK scores for baseline models with and without POS embedding
['[BOLD] Model', '[BOLD] Prompts  [BOLD] 1', '[BOLD] Prompts  [BOLD] 2', '[BOLD] Prompts  [BOLD] 3', '[BOLD] Prompts  [BOLD] 4', '[BOLD] Prompts  [BOLD] 5', '[BOLD] Prompts  [BOLD] 6', '[BOLD] Prompts  [BOLD] 7', '[BOLD] Prompts  [BOLD] 8', '[BOLD] Avg']
4.3.2 Effect of POS Embedding We argue that directly using word embeddings to represent essays in the task of cross-prompt AES is unsuitable due to the issue of the model learning strong semantic representations and leading to overfitting the training data. To investigate this, we replace the word embeddings from the CNN-CNN-MoT and CNN-LSTM-ATT models with POS embeddings and compare the performance.