会议信息

INLG 2019: International Conference on Natural Language Generation

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截稿日期:
2019-07-05
通知日期:
2019-09-01
会议日期:
2019-10-29
会议地点:
Tokyo, Japan
届数:
12
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征稿

INLG 2019 (International Conference on Natural Language Generation) is an academic conference held in Tokyo, Japan on 2019-10-29. The paper submission deadline is 2019-07-05. Acceptance notifications are sent on 2019-09-01.

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
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