A Visual Analytics System for evaluating dataset of Neural Machine Translation

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2023. 2. 서진욱.Neural Machine Translation (신경망을 이용한 기계 번역) 모델을 학습시키는데 있어서 가장 영향을 많이 끼치는 요소는 학습 데이터인 병렬 말뭉치(Parallel Corpora)의 품질이다. 따라서 병렬 말뭉치의 품질 개선이 필수적이며 지금까지 다양한 정제(Refinement) 작업이 많이 도입되었으나 여전히 개선할 부분이 많다. 이 논문은 기계 번역 학습시 필요한 병렬 말뭉치의 품질 개선 작업에 도움이 될 수 있는 시각적 분석 시스템을 소개한다. 우리 시스템은 병렬 말뭉치의 Noise를 빠르게 발견하고 선별하기 위해 머신러닝 기술을 활용하여 다양한 지표 (Metric)를 추출하고 이를 기반으로 상호작용이 가능한 시각적 분석 기법을 제공한다. 사용자는 우리의 시스템을 통해 Noise Data를 손쉽게 파악하고 이에 대한 상세한 내용을 확인 후 제거가 가능하다. 본 시스템의 효율성 및 유용함을 증명하기 위해 4명의 전문가를 포함한 총 8명의 사용자에게 사용성 평가를 진행하였으며, 마지막에 평가 결과를 바탕으로 개선해야 할 점에 대한 논의점도 언급한다.The most important part of training a Neural Machine Translation model maintains good quality of parallel corpora, which are composed of pairs of different languages, Therefore, various refinement tasks have been introduced to improve the quality of parallel corpora, but there is still much room for improvement. This paper introduces a visual analysis system which helps the good quality of parallel corpora for machine translation learning. Our system provides nine different metrics in order to discover and select noise of parallel corpora. Based on our metric and visualization technics, users can find and check noise parallel corpora easily. Our systems effectiveness and usefulness are demonstrated through a qualitative user study with a total of eight users including four experts.제 1 장 서 론 1 제 2 장 관련연구 4 제 3 장 디자인 요구사항 7 제 4 장 데이터 전처리 과정 10 제 5 장 시각화 디자인 14 제 1 절 Distribution View 14 제 2 절 Ranking View 15 제 3 절 Text Compare View 18 제 4 절 Ruleset View 20 제 6 장 사용성 평가 22 제 1 절 결과 23 제 2 절 사후 인터뷰 25 제 7 장 논 의 28 제 8 장 결 론 31 참고문헌 32 Abstract 36석

Machine translation of user-generated content

The world of social media has undergone huge evolution during the last few years. With the spread of social media and online forums, individual users actively participate in the generation of online content in different languages from all over the world. Sharing of online content has become much easier than before with the advent of popular websites such as Twitter, Facebook etc. Such content is referred to as ‘User-Generated Content’ (UGC). Some examples of UGC are user reviews, customer feedback, tweets etc. In general, UGC is informal and noisy in terms of linguistic norms. Such noise does not create significant problems for human to understand the content, but it can pose challenges for several natural language processing applications such as parsing, sentiment analysis, machine translation (MT), etc. An additional challenge for MT is sparseness of bilingual (translated) parallel UGC corpora. In this research, we explore the general issues in MT of UGC and set some research goals from our findings. One of our main goals is to exploit comparable corpora in order to extract parallel or semantically similar sentences. To accomplish this task, we design a document alignment system to extract semantically similar bilingual document pairs using the bilingual comparable corpora. We then apply strategies to extract parallel or semantically similar sentences from comparable corpora by transforming the document alignment system into a sentence alignment system. We seek to improve the quality of parallel data extraction for UGC translation and assemble the extracted data with the existing human translated resources. Another objective of this research is to demonstrate the usefulness of MT-based sentiment analysis. However, when using openly available systems such as Google Translate, the translation process may alter the sentiment in the target language. To cope with this phenomenon, we instead build fine-grained sentiment translation models that focus on sentiment preservation in the target language during translation