CLDFBench: Give Your Cross-Linguistic Data a Lift

Robert Forkel, Johann-Mattis List


Abstract
While the amount of cross-linguistic data is constantly increasing, most datasets produced today and in the past cannot be considered FAIR (findable, accessible, interoperable, and reproducible). To remedy this and to increase the comparability of cross-linguistic resources, it is not enough to set up standards and best practices for data to be collected in the future. We also need consistent workflows for the “retro-standardization” of data that has been published during the past decades and centuries. With the Cross-Linguistic Data Formats initiative, first standards for cross-linguistic data have been presented and successfully tested. So far, however, CLDF creation was hampered by the fact that it required a considerable degree of computational proficiency. With cldfbench, we introduce a framework for the retro-standardization of legacy data and the curation of new datasets that drastically simplifies the creation of CLDF by providing a consistent, reproducible workflow that rigorously supports version control and long term archiving of research data and code. The framework is distributed in form of a Python package along with usage information and examples for best practice. This study introduces the new framework and illustrates how it can be applied by showing how a resource containing structural and lexical data for Sinitic languages can be efficiently retro-standardized and analyzed.
Anthology ID:
2020.lrec-1.864
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6995–7002
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.864
DOI:
Bibkey:
Cite (ACL):
Robert Forkel and Johann-Mattis List. 2020. CLDFBench: Give Your Cross-Linguistic Data a Lift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6995–7002, Marseille, France. European Language Resources Association.
Cite (Informal):
CLDFBench: Give Your Cross-Linguistic Data a Lift (Forkel & List, LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.864.pdf
Code
 cldf/cldfbench