1.6k citations · 8k across the 93 of their papers we have counts for
19 papers · 1 filter
UnNatural Language Inference
Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau +1
Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to…
How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics
Prasanna Parthasarathi, Joelle Pineau, Sarath Chandar
Though generative dialogue modeling is widely seen as a language modeling task, the task demands an agent to have a complex natural language understanding of its input text to carr…
Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System
Ekaterina Kochmar, Dung Do Vu, Robert Belfer +3
We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learnin…
Learning an Unreferenced Metric for Online Dialogue Evaluation
Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang +3
Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to devel…
Compositional Language Understanding with Text-based Relational Reasoning
Koustuv Sinha, Shagun Sodhani, William L. Hamilton +1
Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference. However, it is also crucial to und…
The RLLChatbot: a solution to the ConvAI challenge
Nicolas Gontier, Koustuv Sinha, Peter Henderson +4
Current conversational systems can follow simple commands and answer basic questions, but they have difficulty maintaining coherent and open-ended conversations about specific topi…
Language GANs Falling Short
Massimo Caccia, Lucas Caccia, William Fedus +3
Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Likelihood (MLE) models trained with teache…
Extending Neural Generative Conversational Model using External Knowledge Sources
Prasanna Parthasarathi, Joelle Pineau
The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often…
A Deep Reinforcement Learning Chatbot (Short Version)
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
Ethical Challenges in Data-Driven Dialogue Systems
Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier +4
The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that a…
A Deep Reinforcement Learning Chatbot
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Ryan Lowe, Michael Noseworthy, Iulian V. Serban +3
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and co…
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…
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…
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
Iulian Vlad Serban, Ryan Lowe, Peter Henderson +2
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue syst…
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio +2
We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses…
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
Ryan Lowe, Nissan Pow, Iulian Serban +1
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This…