Version 1
: Received: 3 April 2021 / Approved: 5 April 2021 / Online: 5 April 2021 (12:47:51 CEST)
How to cite:
Rueter, J.; Hämäläinen, M. Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose. Preprints2021, 2021040131. https://doi.org/10.20944/preprints202104.0131.v1
Rueter, J.; Hämäläinen, M. Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose. Preprints 2021, 2021040131. https://doi.org/10.20944/preprints202104.0131.v1
Rueter, J.; Hämäläinen, M. Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose. Preprints2021, 2021040131. https://doi.org/10.20944/preprints202104.0131.v1
APA Style
Rueter, J., & Hämäläinen, M. (2021). Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose. Preprints. https://doi.org/10.20944/preprints202104.0131.v1
Chicago/Turabian Style
Rueter, J. and Mika Hämäläinen. 2021 "Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose" Preprints. https://doi.org/10.20944/preprints202104.0131.v1
Abstract
This paper presents the current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research. We seek to illustrate and outline initial problem types to be encountered in the construction of an Apertium-based shallow-transfer machine translation system for the Mordvin language forms. We indicate reference points within Mordvin Studies and other parts of Uralic studies, as a point of departure for outlining a linguistic studies with a means for measuring its own progress and developing a roadmap for further studies.
Erzya, Moksha, Uralic, Shallow-transfer machine translation, Measurable language research, Measurable language distance, Finite-State Morphology, Universal Dependencies
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
