  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. We also carefully examine the traditional and
BERT-based models on both SCT v1.0 and v1.5 with interesting findings that may
potentially help future studies.

Computation and Language