An RNN-based Binary Classifier for the Story Cloze Test

2017-04-01 · WS 2017 4 · Melissa Roemmele, Sosuke Kobayashi, Naoya Inoue, Andrew Gordon

The Story Cloze Test consists of choosing a sentence that best completes a story given two choices. In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct. The classifier is trained to distinguish correct story endings given in the training data from incorrect ones that we artificially generate. Our experiments evaluate different methods for generating these negative examples, as well as different embedding-based representations of the stories. Our best result obtains 67.2{\%} accuracy on the test set, outperforming the existing top baseline of 58.5{\%}.

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