Improved Deep Learning Baselines for Ubuntu Corpus Dialogs

This paper presents results of our experiments using the Ubuntu Dialog Corpus – the largest publicly available multi-turn dialog corpus. First, we use an in-house implementation of previously reported models to do an independent evaluation using the same data. Second, we evaluate the performances of various LSTMs, Bi-LSTMs and CNNs on the dataset. Third, we create an ensemble by averaging predictions of multiple models. The ensemble further improves the performance and it achieves a state-of-the-art result for this dataset. Finally, we discuss our future plans using this corpus.