Workshop on Multilingual Surface Realization

Event Notification Type: 
Call for Papers
Abbreviated Title: 
Thursday, 19 July 2018 to Friday, 20 July 2018
Contact Email: 
Simon Mille
Submission Deadline: 
Sunday, 8 April 2018

Natural Language Generation (NLG) is in the ascendant both as a stand-alone data-to-text or text-to-text task and as part of downstream applications (see, e.g., abstractive summarization, dialogue-based interaction, question answering, etc.). However, when compared to, e.g., parsing or machine translation, NLG still lags behind in terms of theoretical advances. Thus, while recent years witnessed a shift of the processing paradigm in these areas from traditional supervised machine learning techniques to deep learning techniques, NLG did not arrive there fully yet. Similarly, NLG still does not make full use of the available resources in the way, e.g., parsing does. For instance, the multilingual Universal Dependencies (UD) dataset has already been used for the CoNLL'17 parsing shared task. This dataset facilitates the development of large scale applications that work potentially across all of the UD treebank languages in a uniform fashion.

MSR-WS aims to change the situation and put NLG, and, in particular, surface generation, onto the main stream research agenda of Computational Linguistics, bringing together communities that hardly collaborated so far. It will provide a forum for the presentation of the results of the currently open multilingual Surface Realization Shared Task 2018 (SR’18) and of high quality papers on surface realization and related topics. To encourage inclusiveness and the presentation of speculative and recent work, inclusion in the conference proceedings will be made optional. The author’s preference should be indicated with the final submission.

MSR-WS solicits contributions on all topics that are related to surface realization in NLG. Sought are presentations of cutting edge approaches that address problems of surface-oriented generation such as grammatical and/or information structure-driven word order determination, inflection, functional word determination, paraphrasing, etc. The presented works are expected to be a clear contribution to the progress in robust multilingual surface generation, i.e., be language-independent or easily portable from one language to another and clearly scalable. The topics of interest include, but are not limited to:

Linearization in NLG
Multilingual approaches to surface realization
Function word generation
Inflection in NLG
Joint generation from abstract representations
Surface-oriented text simplification
Surface-oriented spoken language generation
Application of surface realization for grammatical error correction
NLG in surface-oriented paraphrasing
Deep learning approaches to NLG

Programme Committee:

Miguel Ballesteros, IBM Research, USA
Anders Björkelund, University of Stuttgart, Germany
Johan Bos, University of Groningen, Netherlands
Robert Dale, Macquarie University, Australia
Katja Filipova, Google Research, Switzerland
Claire Gardent, CNRS, LORIA, France
Kim Gerdes, Sorbonne Nouvelle, France
Yannis Konstas, Heriot Watt University, UK
Emiel Krahmer, Tilburg University, Netherlands
Mirella Lapata, University of Edinburgh, UK
Jonathan May, Information Sciences Institute, USA
David McDonald, Sift Inc., USA
Ryan McDonald, Google Research, USA
Detmar Meurers, University of Tübingen, Germany
Alexis Nasr, University of Aix Marseille, France
Joakim Nivre, Uppsala University, Sweden
Stephan Oepen, University of Oslo, Norway
Horacio Saggion, Pompeu Fabra University, Spain
Lucia Specia, University of Sheffield, UK
Kees Van Deemter, University of Aberdeen, UK
Sina Zarrieß, University of Bielefeld, Germany
Yue Zhang, Singapore University of Technology and Design, Singapore


Simon Mille, Bernd Bohnet, Leo Wanner, Anya Belz, Emily Pitler