The RGNLP Machine Translation Systems for WAT 2018
Computation and Language
2018-12-04 v1
Abstract
This paper presents the system description of Machine Translation (MT) system(s) for Indic Languages Multilingual Task for the 2018 edition of the WAT Shared Task. In our experiments, we (the RGNLP team) explore both statistical and neural methods across all language pairs. (We further present an extensive comparison of language-related problems for both the approaches in the context of low-resourced settings.) Our PBSMT models were highest score on all automatic evaluation metrics in the English into Telugu, Hindi, Bengali, Tamil portion of the shared task.
Cite
@article{arxiv.1812.00798,
title = {The RGNLP Machine Translation Systems for WAT 2018},
author = {Atul Kr. Ojha and Koel Dutta Chowdhury and Chao-Hong Liu and Karan Saxena},
journal= {arXiv preprint arXiv:1812.00798},
year = {2018}
}
Comments
Short-Paper at WAT Shared Task 2018, In Proceedings of the 5th Workshop on Asian Translation (WAT2018), Hong Kong, China, December