Discriminative Sentence Modeling for Story Ending Prediction

Understand

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge.

• To tackle this task, we propose a new neural network called Diff-Net for better modeling the differences of each ending in this task.
• The proposed model could discriminate two endings in three semantic levels: contextual representation, story-aware representation, and discriminative representation.
• Experimental results on the Story Cloze Test dataset show that the proposed model siginificantly outperforms various systems by a large margin, and detailed ablation studies are given for better understanding our model.