This paper presents results of our experiments for the next utterance ranking on 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 the next utterance ranking on this dataset. Finally, we discuss our future plans using this corpus.
REF evaluate the performances of various models on the dataset, such as LSTMs, Bi-LSTMs, and CNNs.
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
ArXiv
ArXiv
Computer Science