UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study
Source abstract
Abstract
Machine translation (MT) has recently attracted much research on various advanced techniques (i.e., statistical-based and deep learning-based) and achieved great results for popular languages. However, the research on it involving low-resource languages such as Korean often suffer from the lack of openly available bilingual language resources. In this research, we built the open extensive parallel corpora for training MT models, named Ulsan parallel corpora (UPC). Currently, UPC contains two parallel corpora consisting of Korean-English and Korean-Vietnamese datasets. The Korean-English dataset has over 969 thousand sentence pairs, and the Korean-Vietnamese parallel corpus consists of over 412 thousand sentence pairs. Furthermore, the high rate of homographs of Korean causes an ambiguous word issue in MT. To address this problem, we developed a powerful word-sense annotation system based on a combination of sub-word conditional probability and knowledge-based methods, named UTagger. We applied UTagger to UPC and used these corpora to train both statistical-based and deep learning-based neural MT systems. The experimental results demonstrated that using UPC, high-quality MT systems (in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score) can be built. Both UPC and UTagger are available for free download and usage.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Natural Language Processing Techniques · Topic Modeling · Biomedical Text Mining and Ontologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of IT Convergence, University of Ulsan, Ulsan 44610, Korea
Funding
the Ministry of Education of the Republic of Korea, the National Research Foundation of Korea((NRF-2019S1A5B6102698)), and the ICT R&D Program of MSIP/IITP (Development of Core Technology for Context-aware Deep-Symbolic Hybrid Learning and Construction of
References (52)
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