Effect of Word Sense Disambiguation on Neural Machine Translation: A Case Study in Korean

Abstract

With the advent of robust deep learning, neural machine translation (NMT) has achieved great progress and recently become the dominant paradigm in machine translation (MT). However, it is still confronted with the challenge of word ambiguities that force NMT to choose among several translation candidates that represent different senses of an input word. This research presents a case study using Korean word sense disambiguation (WSD) to improve NMT performance. First, we constructed a Korean lexical semantic network (LSN) as a large-scale lexical semantic knowledge base. Then, based on the Korean LSN, we built a Korean WSD preprocessor that can annotate the correct sense of Korean words in the training corpus. Finally, we conducted a series of translation experiments using Korean-English, Korean-French, Korean-Spanish, and Korean-Japanese language pairs. The experimental results show that our Korean WSD system can significantly improve the translation quality of NMT in terms of the BLEU, TER, and DLRATIO metrics. On average, it improved the precision by 2.94 BLEU points and improved translation error prevention by 4.04 TER points and 4.51 DLRATIO points for all the language pairs.