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Generative Deep Neural Networks for Dialogue: A Short Review
Iulian Vlad Serban, Ryan Lowe, Laurent Charlin +1
Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising…
Learning Robust Features using Deep Learning for Automatic Seizure Detection
Pierre Thodoroff, Joelle Pineau, Andrew Lim
We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure mani…
An Actor-Critic Algorithm for Sequence Prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu +5
We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Current log-likelihood training methods are li…
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe +4
Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an effort to model t…
On the Evaluation of Dialogue Systems with Next Utterance Classification
Ryan Lowe, Iulian V. Serban, Mike Noseworthy +2
An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data. Recent work has propo…
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
Chia-Wei Liu, Ryan Lowe, Iulian V. Serban +3
We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation…