@ ICML 2017, Sydney, Australia
Abstract

Research on natural language generation is rapidly growing due to the increasing demand for human-machine communication in natural language. This workshop aims to promote the discussion, exchange, and dissemination of ideas on the topic of text generation, touching several important aspects in this modality: learning schemes and evaluation, model design and structures, advanced decoding strategies, and natural language generation applications. This workshop aims to be a venue for the exchange of ideas regarding data-driven machine learning approaches for text generation, including mainstream tasks such as dialogue generation, instruction generation, and summarization; and for establishing new directions and ideas with potential for impact in the fields of machine learning, deep learning, and NLP.


Invited Speakers


    Tim Baldwin, University of Melbourne
    Trevor Cohn, University of Melbourne
    Mark Johnson, Macquarie University
    André F. T. Martins, Unbabel
    Joelle Pineau, McGill University     Dani Yogatama, DeepMind


Important Dates
Paper submission:   May 26, 2017 June 2, 2017 (23:59 PST)
Author notification:  June 16, 2017
Workshop date: August 10


Sponsors
https://deepmind.com/
https://www.bloomberg.com
http://www.maluuba.com/