Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art.
@article{arxiv.1805.06122,
title = {Narrative Modeling with Memory Chains and Semantic Supervision},
author = {Fei Liu and Trevor Cohn and Timothy Baldwin},
journal= {arXiv preprint arXiv:1805.06122},
year = {2018}
}