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The performance of Neural Machine Translation (NMT) depends significantly onthe size of the available parallel corpus. Due to this fact, low resourcelanguage pairs demonstrate low translation performance compared to highresource language pairs. The translation quality further degrades when NMT isperformed for morphologically rich languages. Even though the web contains alarge amount of information, most people in Sri Lanka are unable to read andunderstand English properly. Therefore, there is a huge requirement oftranslating English content to local languages to share information amonglocals. Sinhala language is the primary language in Sri Lanka and building anNMT system that can produce quality English to Sinhala translations isdifficult due to the syntactic divergence between these two languages under lowresource constraints. Thus, in this research, we explore effective methods ofincorporating Part of Speech (POS) tags to the Transformer input embedding andpositional encoding to further enhance the performance of the baseline Englishto Sinhala neural machine translation model.

Improving English to Sinhala Neural Machine Translation using Part-of-Speech Tag