DEMN: Distilled-Exposition Enhanced Matching Network for Story Comprehension
This addresses story comprehension for natural language processing, but it is incremental as it builds on existing methods for a specific task.
The paper tackled story comprehension by proposing a Distilled-Exposition Enhanced Matching Network (DEMN) for the story-cloze test, achieving state-of-the-art accuracies of 80.1% for a single model and 81.2% for an ensemble model on the ROCStories Corpus.
This paper proposes a Distilled-Exposition Enhanced Matching Network (DEMN) for story-cloze test, which is still a challenging task in story comprehension. We divide a complete story into three narrative segments: an \textit{exposition}, a \textit{climax}, and an \textit{ending}. The model consists of three modules: input module, matching module, and distillation module. The input module provides semantic representations for the three segments and then feeds them into the other two modules. The matching module collects interaction features between the ending and the climax. The distillation module distills the crucial semantic information in the exposition and infuses it into the matching module in two different ways. We evaluate our single and ensemble model on ROCStories Corpus \cite{Mostafazadeh2016ACA}, achieving an accuracy of 80.1\% and 81.2\% on the test set respectively. The experimental results demonstrate that our DEMN model achieves a state-of-the-art performance.