Conference Information
INLG 2019: International Conference on Natural Language Generation
http://www.inlg2019.com
Submission Date:
2019-07-05
Notification Date:
2019-09-01
Conference Date:
2019-10-29
Location:
Tokyo, Japan
Years:
12
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Call For Papers
The 12th International Conference on Natural Language Generation (INLG 2019) organised by the Artificial Intelligence Research Center of Japan (AIRC, AIST), will be held at the National Museum of Emerging Science and Innovation (Miraikan) in Tokyo, Japan, October 29 - November 1, 2019. The conference takes place immediately prior to EMNLP-IJCNLP 2019, which will be held in Hong Kong starting November 3.

We invite the submission of long and short papers, as well as system demonstrations, related to all aspects of Natural Language Generation (NLG), including data-to-text, concept-to-text, text-to-text and vision-to-text approaches. Accepted papers will be presented as oral talks or posters.

The event is organised under the auspices of the Special Interest Group on Natural Language Generation (SIGGEN) of the Association for Computational Linguistics (ACL). 

Topics

INLG 2019 solicits papers on any topic related to NLG. General topics of interest include, but are not limited to:

    Affect/emotion generation
    Applications for people with disabilities
    Cognitive modelling of language production
    Content and text planning
    Corpora for NLG
    Ethical considerations of NLG
    Deep learning models for NLG
    Evaluation of NLG systems
    Generalizability of NLG systems
    Grounded language generation
    Lexicalisation
    Multimedia and multimodality in generation
    Storytelling and narrative generation
    NLG and accessibility
    NLG in dialogue
    NLG for embodied agents and robots
    NLG for real-world applications
    Paraphrasing and Summarisation
    Personalisation and variation in text
    Referring expression generation
    Resources for NLG
    Surface realisation
    Systems architecture
    NLG and Machine Translation
    NLG and speech synthesis
    Natural Language Understanding Techniques for NLG
Last updated by Dou Sun in 2019-07-06
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