603 citations · 1.2k across the 21 of their papers we have counts for
8 papers · 1 filter
Piecewise Latent Variables for Neural Variational Text Processing
Iulian V. Serban, Alexander G. Ororbia, Joelle Pineau +1
Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The…
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…
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
Iulian Vlad Serban, Tim Klinger, Gerald Tesauro +4
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic…
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…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
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…
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian Vlad Serban, Alberto García-Durán, Caglar Gulcehre +4
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scal…