Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
Table 1: Mean Accuracy comparison on the Accidents and Earthquakes data for the order discrimination task. The reference models are Entity-Grid [Barzilay and Lapata2008], HMM [Louis and Nenkova2012], Graph [Guinaudeau and Strube2013], Window network [Li and Hovy2014] and sequence-to-sequence [Li and Jurafsky2016], respectively.
['[EMPTY]', 'Entity-Grid', 'HMM', 'Graph', 'Window (Recurrent)', 'Window (Recursive)', 'Seq2seq', 'Ours']
The entity grid model provides a strong baseline on the Accidents dataset, only outperformed by our model and \citeauthorli2016neural \shortciteli2016neural. On the Earthquakes data the window approach of \citeauthorli2016neural \shortciteli2016neural performs strongly. Our approach outperforms prior models on both datasets, achieving near perfect performance on the Earthquakes dataset.