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Underspecified universal dependency structures as inputs for multilingual surface realisation

Mille, Simon orcid logoORCID: 0000-0002-8852-2764, Belz, Anya orcid logoORCID: 0000-0002-0552-8096, Bohnet, Bernd and Wanner, Leo orcid logoORCID: 0000-0002-9446-3748 (2018) Underspecified universal dependency structures as inputs for multilingual surface realisation. In: 11th International Conference on Natural Language Generation, 5-8 Nov 2018, Tilburg, The Netherlands.

Abstract
In this paper, we present the datasets used in the Shallow and Deep Tracks of the First Multilingual Surface Realisation Shared Task (SR’18). For the Shallow Track, data in ten languages has been released: Arabic, Czech, Dutch, English, Finnish, French, Italian, Portuguese, Russian and Spanish. For the Deep Track, data in three languages is made available: English, French and Spanish. We describe in detail how the datasets were derived from the Universal Dependencies V2.0, and report on an evaluation of the Deep Track input quality. In addition, we examine the motivation for, and likely usefulness of, deriving NLG inputs from annotations in resources originally developed for Natural Language Understanding (NLU), and assess whether the resulting inputs supply enough information of the right kind for the final stage in the NLG process.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Computational linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Krahmer, Emiel, Gatt, Albert and Goudbeek, Martijn, (eds.) Proceedings of the 11th International Conference on Natural Language Generation. . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://doi.org/10.18653/v1/W18-6527
Copyright Information:© 2018 Association for Computational Linguistics
Funders:European Commission: V4Design (H2020-779962-RIA), TENSOR (H2020-700024-RIA), and beAWARE (H2020-700475-RIA).
ID Code:28623
Deposited On:07 Jul 2023 10:46 by Anya Belz . Last Modified 12 Jul 2023 11:14
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