CreoleVal: Multilingual Multitask Benchmarks for Creoles

Author:

Lent Heather1,Tatariya Kushal2,Dabre Raj3,Chen Yiyi4,Fekete Marcell4,Ploeger Esther4,Zhou Li56,Armstrong Ruth-Ann7,Eijansantos Abee8,Malau Catriona9,Heje Hans Erik4,Lavrinovics Ernests4,Kanojia Diptesh10,Belony Paul11,Bollmann Marcel12,Grobol Loïc13,Lhoneux Miryam de2,Hershcovich Daniel5,DeGraff Michel14,Søgaard Anders5,Bjerva Johannes4

Affiliation:

1. Aalborg University, Denmark. hcle@cs.aau.dk

2. KU Leuven, Belgium

3. National Institute of Information and Communications Technology, Japan

4. Aalborg University, Denmark

5. University of Copenhagen, Denmark

6. University of Electronic Science and Technology of China, China

7. Meta, USA

8. Zamboanga State College of Marine Sciences and Technology, Philippines

9. University of Newcastle, Australia

10. University of Surrey, UK

11. Kean University, USA

12. Linköping University, Sweden

13. Université Paris Nanterre, France

14. Massachusetts Institute of Technology, USA

Abstract

Abstract Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research. While the genealogical ties between Creoles and a number of highly resourced languages imply a significant potential for transfer learning, this potential is hampered due to this lack of annotated data. In this work we present CreoleVal, a collection of benchmark datasets spanning 8 different NLP tasks, covering up to 28 Creole languages; it is an aggregate of novel development datasets for reading comprehension relation classification, and machine translation for Creoles, in addition to a practical gateway to a handful of preexisting benchmarks. For each benchmark, we conduct baseline experiments in a zero-shot setting in order to further ascertain the capabilities and limitations of transfer learning for Creoles. Ultimately, we see CreoleVal as an opportunity to empower research on Creoles in NLP and computational linguistics, and in general, a step towards more equitable language technology around the globe.

Publisher

MIT Press

Reference125 articles.

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3. MasakhaNER: Named entity recognition for African languages;Adelani;Transactions of the Association for Computational Linguistics,2021

4. If all you have is a bit of the Bible: Learning POS taggers for truly low-resource languages;Agić,2015

5. Multilingual projection for parsing truly low-resource languages;Agić;Transactions of the Association for Computational Linguistics,2016

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