# UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study
## 1 Introduction  
A MT system that can automatically translate text written in a language into another has been a dream from the beginning of artificial intelligence history. Recently, the research of MT has been energized by the emergence of statistical and deep-learning methodologies. Several open-source toolkits were released to benefit the MT community, such as Moses [1], cdec [2], and Phrasal [3], for statistical MT (SMT), and then OpenNMT [4], Nematus [5], and Tensor2 Tensor [6] for deep-learning neural MT (NMT), which has, to a large extent, brought the dream to reality. Besides the MT methodologies, the parallel corpus is another very crucial component of MT systems. An excellent MT system needs a large parallel corpus to ensure high accuracy during translation model training. The manually compiled parallel corpora, which consume a lot of time and budget to establish, are commonly used for economic purposes. There are some automatically collected parallel corpora available for research [7; 8; 9] but only for popular languages. Since parallel corpora are essential resources for not only MT but, also, a variety of natural language-processing duties (i.e., semantic resources production and multilingual information extraction), many groups have built parallel corpora, which are in use today. Some of which include Europarl ([[http://www.statmt.org/europarl](http://www.statmt.org/europarl)]([http://www.statmt.org/europarl](http://www.statmt.org/europarl))) [7], consisting of English and 20 European languages; JRC-Acquis ([[https://wt-public.emmu.eu/Acquis/JRC-Acquis.3.0/](https://wt-public.emmu.eu/Acquis/JRC-Acquis.3.0/)]([https://wt-public.emmu.eu/Acquis/JRC-Acquis.3.0/](https://wt-public.emmu.eu/Acquis/JRC-Acquis.3.0/))doc/README_Acquis-Communautaire-corpus_JRC.html) [8], containing 20 official European Union languages; and the United Nations corpus ([[http://uncorpora.org](http://uncorpora.org)]([http://uncorpora.org](http://uncorpora.org))) [9], with Arabic, French, Spanish, French, Russian, Chinese, and English. These corpora are freely provided for research purposes. For the Korean-English parallel corpus, several research groups have generated the parallel corpora to train their MT systems. [PERSON] et al. [10] built a corpus with 46,185 sentence pairs by manually collecting texts from travel guidebooks. [PERSON] et al. [11] collected about 300,000 sentence pairs from bilingual news broadcasting websites. [PERSON] and [PERSON] [12] also collected the Korean-English alignment sentences from websites and got approximately 60,000 sentence pairs. These collected parallel corpora are not public, and their sizes are inefficient to train high-quality MT systems. There are a few available Korean-English parallel corpora. The Sejong parallel corpora [13] contains about 60,000 Korean-English sentence pairs collected from diverse sources such as novels, transcribed speech documents, and government documents. KAIST Corpus ([[http://semanticweb.kaist.ac.kr/home/index.php/KAIST_Corpus](http://semanticweb.kaist.ac.kr/home/index.php/KAIST_Corpus)]([http://semanticweb.kaist.ac.kr/home/index.php/KAIST_Corpus](http://semanticweb.kaist.ac.kr/home/index.php/KAIST_Corpus))) also contains 60,000 Korean-English sentence pairs, but the collection resources are not mentioned. News Commentary corpus ([[https://github.com/jungyeul/korean-parallel-corpora](https://github.com/jungyeul/korean-parallel-corpora)]([https://github.com/jungyeul/korean-parallel-corpora](https://github.com/jungyeul/korean-parallel-corpora)).) [14], which was crawled from the CNN and Yahoo websites, contains approximately 97,000 Korean-English sentence pairs. These parallel corpora are publicly available; however, their sizes are too small to train MT systems. In another study, the open parallel corpora OPUS [15] was proposed with a huge number of sentence pairs for multiple languages, including Korean-English pairs.