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This paper presents results of our experiments for the next utterance rankingon the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialogcorpus. First, we use an in-house implementation of previously reported modelsto do an independent evaluation using the same data. Second, we evaluate theperformances of various LSTMs, Bi-LSTMs and CNNs on the dataset. Third, wecreate an ensemble by averaging predictions of multiple models. The ensemblefurther improves the performance and it achieves a state-of-the-art result forthe next utterance ranking on this dataset. Finally, we discuss our futureplans using this corpus.

Improved Deep Learning Baselines for Ubuntu Corpus Dialogs