# UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study
## 1 Introduction
The Korean-English dataset was obtained from technical documents (i.e., Ubuntu and GNOME) and movie subtitles, but the number of sentence pairs of this dataset is limited and contains numerous noises. For the Korean-Vietnamese language pair, Nanyang Technological University NTU-MC [16] introduced multilingual corpora including Korean and Vietnamese sentences. [PERSON] et al. [17] built a Vietnamese-Korean parallel corpus for their own MT systems. However, both datasets are extremely small, with only 15,000 and 24,000 sentence pairs, respectively. The computational linguistics center (University of Science, Ho Chi Minh City) provided a Korean-Vietnamese bilingual corpus with 500,000 sentence pairs for commercial purposes [18]. In terms of applying word-sense disambiguation (WSD) on MT, there have been many research papers on addressing ambiguous words in MT by integrating a WSD system into SMT systems. Their results showed that WSD considerably improves the translation performance for various language pairs--for instance, English-Slovene [19], Czech-English [20], Chinese-English [21; 22], and English-Portuguese [23]. For NMT systems, [PERSON] and [PERSON] [24] explored on WSD abilities in NMT and concluded that ambiguous words significantly reduce the translation quality. They provided metrics to evaluate the WSD performance of NMT systems, but they did not propose any solution to address this issue. After that, [PERSON] et al. [25] used context-awareness embeddings, and [PERSON] et al. [26] added the word-sense to the word embeddings to improve the WSD performance in NMT systems. [PERSON] and [PERSON] [27] improved the lexical choice in NMT by integrating a lexical module into the NMT model so that its output layer could directly generate a target sentence based on the source sentences. In this research, we propose parallel corpora called UPC, consisting of two large parallel open corpora for training Korean-English and Korean-Vietnamese MT models. \"U\" stands for Ulsan (i.e., University of Ulsan, our affiliation), and PC represents parallel corpora. The data is collected for many different audiences, and the topics focus on issues related to everyday life, such as economics, education, religion, etc. The sources used to extract these parallel corpora are mainly articles of multilingual magazines and example sentences of online dictionaries. Up to 969 thousand and more than 412 thousand sentence pairs in Korean-English and Korean-Vietnamese, respectively, were obtained. These datasets are large enough to train quality MT systems and are available for download at [[https://github.com/haivv/UPC](https://github.com/haivv/UPC)]([https://github.com/haivv/UPC](https://github.com/haivv/UPC)). Besides, the word ambiguities (or homographs), which are spelt the same but have different meanings, reduces the performance of both the SMT [19; 28] and NMT [24; 29]. The word ambiguity problem forces MT systems to choose among several translation candidates representing different senses of a source word. Disambiguating the word-senses is a simple task for humans, but it is an extremely difficult duty for computer systems. To solve this issue, we propose a hybrid approach that combines a sub-word conditional probability and knowledge-based methods to identify the correct senses of homographs and annotate corresponding codes to these homographs. Using this approach, we developed a fast and accurate word-sense annotation system, called UTagger. Then, we applied UTagger to the original Korean sentences in UPC to make our word-sense annotated parallel corpora and used these annotated parallel corpora to train both SMT and NMT systems. The experimental results show that using UPC can build high-quality MT systems, and UTagger can significantly boost the translation performance in terms of the BLEU [30] and TER [31] metrics.