Search Results for author: Ryan Lowe

Found 23 papers, 15 papers with code

Recursively Summarizing Books with Human Feedback

no code implementations22 Sep 2021 Jeff Wu, Long Ouyang, Daniel M. Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, Paul Christiano

Our human labelers are able to supervise and evaluate the models quickly, despite not having read the entire books themselves.

Abstractive Text Summarization Question Answering

Learning to summarize with human feedback

1 code implementation NeurIPS 2020 Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul F. Christiano

We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning.

Learning to summarize from human feedback

1 code implementation2 Sep 2020 Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano

We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning.

Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop

no code implementations21 Jul 2020 Shagun Sodhani, Mayoore S. Jaiswal, Lauren Baker, Koustuv Sinha, Carl Shneider, Peter Henderson, Joel Lehman, Ryan Lowe

This report documents ideas for improving the field of machine learning, which arose from discussions at the ML Retrospectives workshop at NeurIPS 2019.

Learning an Unreferenced Metric for Online Dialogue Evaluation

1 code implementation ACL 2020 Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang, Ryan Lowe, William L. Hamilton, Joelle Pineau

Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue.

Dialogue Evaluation

On the interaction between supervision and self-play in emergent communication

1 code implementation ICLR 2020 Ryan Lowe, Abhinav Gupta, Jakob Foerster, Douwe Kiela, Joelle Pineau

A promising approach for teaching artificial agents to use natural language involves using human-in-the-loop training.

Seeded self-play for language learning

no code implementations WS 2019 Abhinav Gupta, Ryan Lowe, Jakob Foerster, Douwe Kiela, Joelle Pineau

Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations, including populations speaking human language.

Imitation Learning Meta-Learning

Ethical Challenges in Data-Driven Dialogue Systems

1 code implementation24 Nov 2017 Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, Joelle Pineau

The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm.

reinforcement-learning

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

no code implementations EMNLP 2017 Teng Long, Emmanuel Bengio, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same.

Language Modelling Reading Comprehension

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

no code implementations1 Jan 2017 Ryan Lowe, Nissan Pow, Iulian Vlad Serban, Laurent Charlin, Chia-Wei Liu, Joelle Pineau

In this paper, we analyze neural network-based dialogue systems trained in an end-to-end manner using an updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.

Conversation Disentanglement Feature Engineering

Generative Deep Neural Networks for Dialogue: A Short Review

no code implementations18 Nov 2016 Iulian Vlad Serban, Ryan Lowe, Laurent Charlin, Joelle Pineau

Researchers have recently started investigating deep neural networks for dialogue applications.

Response Generation

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

9 code implementations19 May 2016 Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, Yoshua Bengio

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue.

Response Generation

Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data

no code implementations ACL 2016 Teng Long, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup

Recent work in learning vector-space embeddings for multi-relational data has focused on combining relational information derived from knowledge bases with distributional information derived from large text corpora.

Entity Embeddings

On the Evaluation of Dialogue Systems with Next Utterance Classification

no code implementations WS 2016 Ryan Lowe, Iulian V. Serban, Mike Noseworthy, Laurent Charlin, Joelle Pineau

An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data.

Classification General Classification

A Survey of Available Corpora for Building Data-Driven Dialogue Systems

4 code implementations17 Dec 2015 Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models.

Transfer Learning

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