Co-Attention Based Neural Network for Source-Dependent Essay Scoring
Table 4: The performance (QWK) of the baselines and our model. ∗ indicates that the model QWK is significantly better than the SELF-ATTN (p<0.05). † indicates that the model QWK is significantly better than the SG (p<0.05). The best results in each row are in bold.
['[BOLD] Prompts', '[BOLD] SVR', '[BOLD] BLRR', '[BOLD] SG', '[BOLD] SELF-ATTN', '[BOLD] CO-ATTN']
We first examine H1. The CO-ATTN model yields higher performance than the SELF-ATTN model on all ASAP prompts. However, the CO-ATTN model only significantly outperforms the SELF-ATTN model on Prompt 3. Second, we examine H2. The CO-ATTN model yields higher performance than the SELF-ATTN model, significantly on both of the RTA corpora. Last, we examine H3. The CO-ATTN model yields higher performance than all non-neural network baselines.