Indonesian is an agglutinative language since it has a compounding process of
word-formation. Therefore, the translation model of this language requires a
mechanism that is even lower than the word level, referred to as the sub-word
level. This compounding process leads to a rare word problem since the number
of vocabulary explodes. We propose a strategy to address the unique word
problem of the neural machine translation (NMT) system, which uses Indonesian
as a pair language. Our approach uses a rule-based method to transform a word
into its roots and accompanied affixes to retain its meaning and context. Using
a rule-based algorithm has more advantages: it does not require corpus data but
only applies the standard Indonesian rules. Our experiments confirm that this
method is practical. It reduces the number of vocabulary significantly up to
57\%, and on the English to Indonesian translation, this strategy provides an
improvement of up to 5 BLEU points over a similar NMT system that does not use
this technique.

本文提出了一个基于规则的算法，用于解决印尼语作为一个配对语言的神经机器翻译系统中，由于复合词的形态构成导致词汇量爆炸的罕见单词问题，该算法通过将单词转化为其词根和附加的语素来保留其含义和上下文，实验表明，该方法实用性强，可以显著减少语料库数据的需求，并且相对于不使用此技术的类似神经机器翻译系统，在英印翻译上提供了高达 5 个 BLEU 点的改进。

使用印尼语次词分离器减少印尼语词汇量

Reduce Indonesian Vocabularies with an Indonesian Sub-word Separator

Multimodal machine translation is an attractive application of neural machine
translation (NMT). It helps computers to deeply understand visual objects and
their relations with natural languages. However, multimodal NMT systems suffer
from a shortage of available training data, resulting in poor performance for
translating rare words. In NMT, pretrained word embeddings have been shown to
improve NMT of low-resource domains, and a search-based approach is proposed to
address the rare word problem. In this study, we effectively combine these two
approaches in the context of multimodal NMT and explore how we can take full
advantage of pretrained word embeddings to better translate rare words. We
report overall performance improvements of 1.24 METEOR and 2.49 BLEU and
achieve an improvement of 7.67 F-score for rare word translation.

本研究将预训练的词向量与搜索方法相结合，以解决多模态机器翻译中罕见词汇的翻译问题，并通过实验表明，这种方法能够改进机器翻译的性能。