Narrative Modeling with Memory Chains and Semantic Supervision
This addresses story comprehension for NLP applications, but it is incremental as it builds on prior work on the ROC Story Cloze Test.
The paper tackled story ending prediction by proposing a method that tracks semantic aspects with external neural memory chains, achieving superior performance and setting a new state of the art on the ROC Story Cloze Test.
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.