Experiment on a Transformer Model Indonesian-to-Sundanese Neural Machine Translation with Sundanese Speech Level Evaluation

DOI

10.2991/assehr.k.210427.069How to use a DOI?

Keywords

Neural machine translation, Speech level, Sundanese

Abstract

Speech level is one of the essential Sundanese language elements. As Indonesian mixed within Sundanese language use, the usage of speech level is gradually degrading. Indonesian, in order to get correct word choice in Sundanese language, social contexts may refer to many sources such as a dictionary, or thesaurus. However, for better translation in syntax and context, machine translation is offered. Based on the fact, this experiment focuses on the problem when translating Indonesian to Sundanese and the evaluation of Sundanese speech level in the translated texts. Neural machine translation (NMT) was chosen as the current technology in machine translation, which worked by combining recurrent neural network encoder-decoder. The experiment started with building 50.000 Sundanese-Indonesian sentences as a parallel corpus to build and train NMT models. The experiment on sentence training in Transformer NMT without out-of-vocabulary (OOV) shows 42.72% BLEU Score, and Average Training Loss was 1.77 while for speech level was dominated by 56% basa loma (coarse) of the whole testing result.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Restu Bias Primandhika
AU  - Muhammad Nadzeri Munawar
AU  - Aceng Ruhendi Saifullah
PY  - 2021
DA  - 2021/04/28
TI  - Experiment on a Transformer Model Indonesian-to-Sundanese Neural Machine Translation with Sundanese Speech Level Evaluation
BT  - Proceedings of the Thirteenth Conference on Applied Linguistics (CONAPLIN 2020)
PB  - Atlantis Press
SP  - 452
EP  - 459
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.210427.069
DO  - 10.2991/assehr.k.210427.069
ID  - Primandhika2021
ER  -