Discriminative Sentence Modeling for Story Ending Prediction
Table 1: Overall results (accuracy) on Story Cloze Test v1.0 dataset. Our results are depicted in bold face (verified by the significant test with p<0.05). Result with underline means it utilizes pre-trained model. * indicates results from chaturvedi-etal-2017 chaturvedi-etal-2017.
['[BOLD] System', '[BOLD] Test']
As we can see that our Diff-Net achieves an accuracy of 77.8, which outperforms previous system by an absolute gain of 2.6% compared to \citeauthorschwartz-etal-2017 \shortciteschwartz-etal-2017, demonstrating the effectiveness of the proposed model. Note that, the best previous single model by \citeauthorschwartz-etal-2017 \shortciteschwartz-etal-2017 adds additional RNNLM trained on the large-scale training set, while we do not exploit any knowledge from the training data.