Bridging Philippine languages with multilingual neural machine translation
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ID: 285497
2021
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Abstract
The Philippines is home to more than 150 languages that are considered low- resourced, Resulting in a lack of pursuit in developing a translation system for most of its languages. To aid in improving the results and processes of translation systems for low-resource languages, multilingual NMT became an active area of research. However, existing works in multilingual NMT disregard the analysis of a multilingual model on a closely related and low-resource language group in the context of zero-resource translation. In this study, we have benchmarked translation systems for several Philip- pine Languages and provide an analysis of a transformer-based multilingual NMT system for morphologically rich and low-resource languages in terms of its ca- pabilities in translating unseen language pairs using zero-shot translation and pivot-based translation. Our studies show that due to the architectural design of the Transformer model, common words and sentence-length differences affect the performance of a multilingual NMT in translating both seen and unseen lan- guage pairs with Bicolano, Cebuano, and Hiligaynon consistently perform better than the other languages in various translation task by having a good balance of commonality and sentence length difference. This work also investigated the effect of increasing the model size and capacity that allowed the model to build a language invariant shared representation space and stronger decoding capabilities to do zero-shot translation where the previous model with smaller capacity failed to develop a language invariant shared represen- tation space and could only produce translations up to English when attempting a zero-shot translation. Since we are dealing with low-resource multilingual data, some of the risks involved are domain shift and out-of-vocabulary words. We have also shown how the multilingual NMT leverages joint byte-pair encoding and the shared represen- tation space to produce translation for unseen or rare words. Lastly, we have shown that the transformer-based multilingual NMT can com- pete with, or outperform other translation approaches as we have shown in a comparative analysis against the baseline statistical MT models where several statistical-based translation models were produced to compare its performance against a single multilingual NMT model. We have shown in the results that the translation performance of the multilingual NMT is superior to the Statisti- cal MT models both in bidirectional English and Philippine languages translation task and a pivot-based Philippine languages translation task where we have shown the capability of the multilingual NMT model to retain information and context across multilingual translation,
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| Authors | Baliber, Renz Iver D. |
| Journal | Malay Journal |
| Year | 2021 |
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