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The second multilingual surface realisation shared task (SR'19): Overview and evaluation results

Mille, Simon orcid logoORCID: 0000-0002-8852-2764, Anya, Belz orcid logoORCID: 0000-0002-0552-8096, Bohnet, Bernd, Graham, Yvette and Wanner, Leo orcid logoORCID: 0000-0002-9446-3748 (2019) The second multilingual surface realisation shared task (SR'19): Overview and evaluation results. In: 2nd Workshop on Multilingual Surface Realisation (MSR 2019), 3 Nov 2019, Hong Kong, China.

Abstract
We report results from the SR’19 Shared Task, the second edition of a multilingual surface realisation task organised as part of the EMNLP’19 Workshop on Multilingual Surface Realisation. As in SR’18, the shared task comprised two different tracks: (a) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (b) a Deep Track where additionally, functional words and morphological information were removed. The Shallow Track was offered in 11, and the Deep Track in three languages. Systems were evaluated (a) automatically, using a range of intrinsic metrics, and (b) by human judges in terms of readability and meaning similarity to a reference. This report presents the evaluation results, along with descriptions of the SR’19 tracks, data and evaluation methods, as well as brief summaries of the participating systems. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019). . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:http://dx.doi.org/10.18653/v1/D19-6301
Copyright Information:© 2019 Association for Computational Linguistics.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:24159
Deposited On:22 Jan 2020 10:44 by Vidatum Academic . Last Modified 04 Jul 2023 10:37
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