Neural Machine Translation for Cebuano to Tagalog with Subword Unit Translation

TL;DR

This paper presents a neural machine translation system for Cebuano to Tagalog using subword units, achieving moderate BLEU scores and demonstrating improvements with subword translation techniques.

Contribution

Introduces a novel neural translation approach for Philippine languages using subword units, improving translation accuracy for Cebuano to Tagalog.

Findings

BLEU score of 20.01 for baseline translation

BLEU score increased to 22.87 with subword units

Translation remains understandable but not yet highly accurate

Abstract

The Philippines is an archipelago composed of 7, 641 different islands with more than 150 different languages. This linguistic differences and diversity, though may be seen as a beautiful feature, have contributed to the difficulty in the promotion of educational and cultural development of different domains in the country. An effective machine translation system solely dedicated to cater Philippine languages will surely help bridge this gap. In this research work, a never before applied approach for language translation to a Philippine language was used for a Cebuano to Tagalog translator. A Recurrent Neural Network was used to implement the translator using OpenNMT sequence modeling tool in TensorFlow. The performance of the translation was evaluated using the BLEU Score metric. For the Cebuano to Tagalog translation, BLEU produced a score of 20.01. A subword unit translation for verbs and copyable approach was performed where commonly seen mistranslated words from the source to the target were corrected. The BLEU score increased to 22.87. Though slightly higher, this score still indicates that the translation is somehow understandable but is not yet considered as a good translation.