• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.184-189
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• 2017

With the advent of robust deep learning method, Neural machine translation has recently become a dominant paradigm and achieved adequate results in translation between popular languages such as English, German, and Spanish. However, its results in under-resourced languages Korean and Vietnamese are still limited. This paper reports an attempt at constructing a bidirectional Korean-Vietnamese Neural machine translation system with the supporting of Korean analysis tool - UTagger, which includes morphological analyzing, POS tagging, and WSD. Experiment results demonstrate that UTagger can significantly improve translation quality of Korean-Vietnamese NMT system in both translation direction. Particularly, it improves approximately 15 BLEU scores for the translation from Korean to Vietnamese direction and 3.12 BLEU scores for the reverse direction.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.184-189
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• 2017

With the advent of robust deep learning method, Neural machine translation has recently become a dominant paradigm and achieved adequate results in translation between popular languages such as English, German, and Spanish. However, its results in under-resourced languages Korean and Vietnamese are still limited. This paper reports an attempt at constructing a bidirectional Korean-Vietnamese Neural machine translation system with the supporting of Korean analysis tool - UTagger, which includes morphological analyzing, POS tagging, and WSD. Experiment results demonstrate that UTagger can significantly improve translation quality of Korean-Vietnamese NMT system in both translation direction. Particularly, it improves approximately 15 BLEU scores for the translation from Korean to Vietnamese direction and 3.12 BLEU scores for the reverse direction.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.504-509
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• 2018

Neural machine translation (NMT) has recently achieved the state-of-the-art performance. However, it is reported failing in the word sense disambiguation (WSD) for several popular language pairs. In this paper, we explore the extent to which NMT systems are able to disambiguate the Korean homographs. Homographs, words with different meanings but the same written form, cause the word choice problems for NMT systems. Consistent with the popular language pairs, we discover that NMT systems fail to translate Korean homographs correctly. We provide a Korean word sense disambiguation tool-UTagger to use for improvement of NMT's translation quality. We conducted translation experiments using Korean-English and Korean-Vietnamese language pairs. The experimental results show that UTagger can significantly improve the translation quality of NMT in terms of the BLEU, TER, and DLRATIO evaluation metrics.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.280-284
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• 2016

본 논문에서는 기호로 둘러싸인 내포문이 포함된 문장의 번역 성능을 높이는 방법을 제안한다. 입력 문장에서 내포문을 추출하여 여러 문장으로 나타내고, 각각의 문장들을 번역한다. 그리고 번역된 문장들을 복원정보를 활용하여 최종 번역 문장을 생성한다. 이러한 방법론은 입력 문장의 길이를 줄여주며, 그로 인하여 문장 구조가 단순해져 번역 품질이 향상된다. 본 논문에서는 한국어-베트남어 통계 기반 번역기에 대하여 제안한 방법론을 적용하고 실험하였다. 그 결과 BLEU 점수가 약 1.5 향상된 것을 확인할 수 있었다.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.280-284
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• 2016

본 논문에서는 기호로 둘러싸인 내포문이 포함된 문장의 번역 성능을 높이는 방법을 제안한다. 입력 문장에서 내포문을 추출하여 여러 문장으로 나타내고, 각각의 문장들을 번역한다. 그리고 번역된 문장들을 복원정보를 활용하여 최종 번역 문장을 생성한다. 이러한 방법론은 입력 문장의 길이를 줄여주며, 그로 인하여 문장 구조가 단순해져 번역 품질이 향상된다. 본 논문에서는 한국어-베트남어 통계 기반 번역기에 대하여 제안한 방법론을 적용하고 실험하였다. 그 결과 BLEU 점수가 약 1.5 향상된 것을 확인할 수 있었다.