A Story Coherence based Neural Network Model for Predicting Story Ending

Qian Li, Ziwei Li, Jin-Mao Wei, Zhenglu Yang, Yanhui Gu, R. Uday Kiran

Abstract: Predicting the ending of a story is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). Although several studies have addressed this issue, the performance remains unsatisfactory due to ineffectiveness of story comprehension. In this paper, we propose to construct a story coherence based neural network model (SCNN) with well-designed optimizations. The preliminary evaluation demonstrates the effectiveness of our model which is superior to that of state-of-the-art approaches.