영어자원문법을 활용한 신경망 기계번역의 데이터 증강과 성능 평가

Evaluation of Neural Machine Translation Trained by Augmented Data Using English Resource Grammar

초록

Wang, Guehyun; Song, Sanghoun (2021), “Evaluation of Neural Machine Translation Trained by Augmented Data Using English Resource Grammar,” Language and Information Society 42. Machine translation commonly involves both analysis and generation across different human languages. This implies that parallel corpora of a large size are essential to create a theoretically reliable and practically robust translation model. However, as is well known, the parallel corpora between Korean and English are insufficient. In this respect, this study expands the data by means of English Resource Grammar (Flickinger 2000) to improve the translation model between the languages. Then, it looks at whether the neural machine translation model performs better with the augmented data. Unfortunately, it turns out the translation models based on augmented data exhibit rather lower BLEU scores. This study further discusses the reason for the unsatisfactory scores and raises the necessity of human evaluation as a next step.

키워드

기계번역딥러닝데이터 증강영어자원문법다시쓰기병렬 코퍼스평가 방법machine translationdeep learningdata augmentationERGparaphrasingparallel corpusevaluation metric
제목
영어자원문법을 활용한 신경망 기계번역의 데이터 증강과 성능 평가
제목 (타언어)
Evaluation of Neural Machine Translation Trained by Augmented Data Using English Resource Grammar
저자
왕규현송상헌
DOI
10.29211/soli.2021.42..007
발행일
2021
저널명
언어와 정보 사회
42
페이지
179 ~ 200