Since deep learning was introduced, a series of achievements has been published in the field of automatic machine translation (MT). However, Korean-Vietnamese MT systems face many challenges because of a lack of data, multiple meanings of individual words, and grammatical diversity that depends on context. Therefore, the quality of Korean-Vietnamese MT systems is still sub-optimal. This paper discusses a method for applying Named Entity Recognition (NER) and Part-of-Speech (POS) tagging to Vietnamese sentences to improve the performance of Korean-Vietnamese MT systems. In terms of implementation, we used a tool to tag NER and POS in Vietnamese sentences. In addition, we had access to a Korean-Vietnamese parallel corpus with more than 450K paired sentences from our previous research paper. The experimental results indicate that tagging NER and POS in Vietnamese sentences can improve the quality of Korean-Vietnamese Neural MT (NMT) in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score. On average, our MT system improved by 1.21 BLEU points or 2.33 TER scores after applying both NER and POS tagging to the Vietnamese corpus. Due to the structural features of language, the MT systems in the Korean to Vietnamese direction always give better BLEU and TER results than translation machines in the reverse direction.

The construction of annotated corpora requires considerable manual effort. This paper presents a pragmatic method to minimize human intervention for the construction of Korean part-of-speech (POS) tagged corpus. Instead of focusing on improving the performance of conventional automatic POS taggers, we devise a discriminative POS tagger which can selectively produce either a single analysis or multiple analyses based on the tagging reliability. The proposed approach uses two decision rules to judge the tagging reliability. Experimental results show that the proposed approach can effectively control the quality of corpus and the amount of manual annotation by the threshold value of the rule.