A Simple and Effective Approach to the Story Cloze Test

TL;DR

This paper introduces a simple neural method using skip-thought embeddings for the Story Cloze Test, achieving high accuracy without feature engineering and highlighting the importance of the last sentence in the prompt.

Contribution

It presents a fully-neural, feature-engineering-free approach that outperforms previous methods by focusing on the last sentence of the prompt.

Findings

Achieves near state-of-the-art accuracy

Using only the last sentence improves performance

No feature engineering needed

Abstract

In the Story Cloze Test, a system is presented with a 4-sentence prompt to a story, and must determine which one of two potential endings is the 'right' ending to the story. Previous work has shown that ignoring the training set and training a model on the validation set can achieve high accuracy on this task due to stylistic differences between the story endings in the training set and validation and test sets. Following this approach, we present a simpler fully-neural approach to the Story Cloze Test using skip-thought embeddings of the stories in a feed-forward network that achieves close to state-of-the-art performance on this task without any feature engineering. We also find that considering just the last sentence of the prompt instead of the whole prompt yields higher accuracy with our approach.