Ryan Lowe

Towards Policy-Guided Conversational Recommendation with Dialogue Acts

Paul Crook

Y-Lan Boureau

J. Weston

Akbar Karimi

Leonardo Rossi

Andrea Prati

Wenqiang Lei

Xiangnan He

Qingyun Yisong Miao

Richang Wu

Min-Yen Hong

Kan Tat-Seng

Raymond Li

Samira Ebrahimi Kahou

Hannes Schulz

Zujie Liang

Huang Hu

Can Xu

Jian Miao

Lizi Liao … (see 47 more)

Ryuichi Takanobu

Yunshan Ma

Xun Yang

Wenchang Ma

Minlie Huang

Minghao Tu

Iulian Serban

Aaron C. Courville

David Silver

Julian Schrittwieser

K. Simonyan

Ioannis Antonoglou

Aja Huang

A. Guez

Hanlin Zhu

O. Vinyals

Igor Babuschkin

Junyoung Chung

M. Mathieu

Max Jaderberg

Wojciech M. Czar-725 necki

A. Dudzik

Petko Georgiev

Richard Powell

T. Ewalds

Dan Horgan

M. Kroiss

Ivo Danihelka

J. Agapiou

Junhyuk Oh

Valentin Dalibard

David Choi

L. Sifre

Yury Sulsky

Sasha Vezhnevets

James Molloy

Trevor Cai

D. Budden

T. Paine

Caglar Gulçehre

Ziyu Wang

Tobias Pfaff

Tobias Pohlen

2021-12-31

(published)

www.semanticscholar.org

Introduction to NIPS 2017 Competition Track

Sergio Escalera

Markus Weimer

Mikhail Burtsev

Valentin Malykh

Varvara Logacheva

Ryan Lowe

Iulian V. Serban

Yoshua Bengio

Alexander Rudnicky

Alan W. Black

Shrimai Prabhumoye

Łukasz Kidziński

Sharada Prasanna Mohanty

Carmichael F. Ong

Jennifer L. Hicks

Sergey Levine

Marcel Salathé

Scott Delp

Iker Huerga

Alexander Grigorenko … (see 19 more)

Leifur Thorbergsson

Anasuya Das

Kyla Nemitz

Jenna Sandker

Stephen King

Alexander S. Ecker

Leon A. Gatys

Matthias Bethge

Jordan Boyd-Graber

Shi Feng

Pedro Rodriguez

Mohit Iyyer

He He

Hal Daumé III

Sean McGregor

Amir Banifatemi

Alexey Kurakin

Ian G Goodfellow

Samy Bengio

2018-09-27

The NIPS '17 Competition: Building Intelligent Systems (published)

doi.org

The First Conversational Intelligence Challenge

Mikhail Burtsev

Varvara Logacheva

Valentin Malykh

Iulian V. Serban

Ryan Lowe

Shrimai Prabhumoye

Alan W. Black

Alexander Rudnicky

Yoshua Bengio

2018-09-27

The NIPS '17 Competition: Building Intelligent Systems (published)

doi.org

World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions

Teng Long

Emmanuel Bengio

Ryan Lowe

Jackie CK Cheung

Doina Precup

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading compr… (see more)ehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.

2017-08-31

Conference on Empirical Methods in Natural Language Processing (published)

doi.org

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Iulian V. Serban

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterance… (see more)s in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.

2017-02-11

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

arxiv.org

Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus

Iulian Vlad Serban

Chia-Wei Liu

In this paper, we construct and train end-to-end neural network-based dialogue systems usingan updated version of the recent Ubuntu Dialogue… (see more) Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering, which can be both time consuming and expensive. We provide baselines in two different environments: one where models are trained to maximize the log-likelihood of a generated utterance conditioned on the context of the conversation, and one where models are trained to select the correct next response from a list of candidate responses. These are both evaluated on a recall task that we call Next Utterance Classification (NUC), as well as other generation-specific metrics. Finally, we provide a qualitative error analysis to help determine the most promising directions for future research on the Ubuntu Dialogue Corpus, and for end-to-end dialogue systems in general.

2017-01-19

Dialogue & Discourse (published)

doi.org

An Actor-Critic Algorithm for Sequence Prediction

We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Curren… (see more)t log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rather than the ground-truth tokens. We address this problem by introducing a \textit{critic} network that is trained to predict the value of an output token, given the policy of an \textit{actor} network. This results in a training procedure that is much closer to the test phase, and allows us to directly optimize for a task-specific score such as BLEU. Crucially, since we leverage these techniques in the supervised learning setting rather than the traditional RL setting, we condition the critic network on the ground-truth output. We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling.

2016-12-31

ICLR.cc/2017/conference (poster)

doi.org

openreview.net

TRAIL: Responsible AI for Professionals and Leaders

Mila Ventures Founder in Residence

AI Advantage: Productivity in Public Service

Publications

TRAIL: Responsible AI for Professionals and Leaders

Mila Ventures Founder in Residence

AI Advantage: Productivity in Public Service

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Ryan Lowe

Publications