# WCC-JC: A Web-Crawled Corpus for Japanese-Chinese Neural Machine Translation
## 2 Related Works
constructed JParaCrawl, a large-scale Web-based Japanese-English bilingual corpus, by crawling the Web on a large scale, automatically collecting Japanese-English bilingual sentences, and filtering out noisy bilingual pairs [16]. [PERSON] et al. automatically built a corpus by crawling language resources from the Internet, but the data were not well filtered [17]. [PERSON] et al. built a sentence segmentation method for Bengali, a sparse language, and constructed a non-English bilingual corpus [18]. Thus, the construction of an open Japanese-Chinese bilingual corpus for NMT has significant implications on the resource scarcity problem. [PERSON] et al. compared two corpora of Czech. One was a traditional corpus and the other was a Web-crawled corpus, which had been extensively compared and analyzed for quality [19]. The EuroparITV Multimedia Parallel Corpus (EMPAC) was a collection of subtitles in English and Spanish for videos from the European Parliament's Multimedia Centre. The corpus covered a time span from 2009 to 2017 and it was made up of 4000 texts amounting to two and half million tokens for every language, corresponding to approximately 280 h of video [20]. [PERSON] et al. introduced the BSD corpus and the results of the BSD translation task in WAT2020. Additionally, they discussed the challenges of dialogue translation based on the analysis of the translation results [21]. The BSD corpus was constructed using \"dialogue\" in \"business\" as the domain of the bilingual corpus. [PERSON] et al. conducted a parallel corpus in the field of biomedicine for English-Chinese translation [22]. They compared the effectiveness of different algorithms/tools for sentence boundary detection and sentence alignment, and used the constructed corpus, fine-tuning the NMT models. [PERSON] et al. used pretrained language models, but by fine-tuning them on parallel texts with the aim of improving alignment quality, they proposed a method for efficiently extracting alignments from these fine-tuned models and demonstrated that their models can consistently outperform all previous state-of-the-art models of the species [23]. [PERSON] et al. presented an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 96 languages, extracting 135M parallel sentences for 16,720 different language pairs, and achieving strong BLEU scores for many language pairs [24]. For Japanese-Chinese translation, [PERSON] et al. proposed the following three data augmentation methods to improve the quality of Japanese-Chinese NMT: (1) radicals as an additional input feature [25]; (2) the created Chinese character decomposition table [26]; (3) a corpus augmentation approach [27], considering the lack of resources in bilingual corpora. The related works above are sorted by year. We also show the classification of these related works on five aspects: (1) Corpus linguistics; (2) Japanese-Chinese bilingual corpora; (3) Web-crawled corpora; (4) other corpora; (5) corpus augmentation through a summarized Table 1.  
The above related research showed that corpora play an important role in improving translation accuracy and in other directions of language processing. Thus, the construction of a Japanese-Chinese bilingual corpus for NMT has significant implications for the resource \begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Related Works** \\ \hline Corpus linguistics & [3] \\ \hline Japanese-Chinese bilingual corpora & [1], TED talks, [4; 5] \\ \hline Web-crawled corpora & [7; 8; 9; 10; 11; 13; 14; 16; 17; 19; 24] \\ \hline Other corpora & [6; 12; 18; 20; 21; 22] \\ \hline Corpus augmentation & [25; 23; 26; 27] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works. scarcity problem. In this research, we aim to develop Japanese-Chinese machine translation and construct a Japanese-Chinese bilingual corpus of a certain scale.