  The performance of Neural Machine Translation (NMT) depends significantly on
the size of the available parallel corpus. Due to this fact, low resource
language pairs demonstrate low translation performance compared to high
resource language pairs. The translation quality further degrades when NMT is
performed for morphologically rich languages. Even though the web contains a
large amount of information, most people in Sri Lanka are unable to read and
understand English properly. Therefore, there is a huge requirement of
translating English content to local languages to share information among
locals. Sinhala language is the primary language in Sri Lanka and building an
NMT system that can produce quality English to Sinhala translations is
difficult due to the syntactic divergence between these two languages under low
resource constraints. Thus, in this research, we explore effective methods of
incorporating Part of Speech (POS) tags to the Transformer input embedding and
positional encoding to further enhance the performance of the baseline English
to Sinhala neural machine translation model.

Computation and Language;Machine Learning