Makoto Morishita, Katsuki Chousa, Jun Suzuki, Masaaki Nagata &ndash; NTT Communication Science Laboratories, NTT Corporation, Japan
Most current machine translation models are mainly trained with parallel corpora, and their translation accuracy largely depends on the quality and quantity of the corpora. Although there are billions of parallel sentences for a few language pairs, effectively dealing with most language pairs is difficult due to a lack of publicly available parallel corpora. This paper creates a large parallel corpus for English-Japanese, a language pair for which only limited resources are available, compared to such resource-rich languages as English-German. It introduces a new web-based English-Japanese parallel corpus named JParaCrawl v3.0. Our new corpus contains more than 21 million unique parallel sentence pairs, which is more than twice as many as the previous JParaCrawl v2.0 corpus. Through experiments, we empirically show how our new corpus boosts the accuracy of machine translation models on various domains. The JParaCrawl v3.0 corpus will eventually be publicly available online for research purposes.
Our method extracts parallel sentences from the web. Thus, the first step is finding a website that has parallel sentences. This method is based on the hypothesis that websites containing the same English and Japanese sentences might have parallel texts. To list such parallel websites, we analyzed all the Common Crawl text archive data released from March 2019 to August 2021³. [³During this period, the Common Crawl project released 25 archives, and their text size was about 212 TB.] We identified the language in the archive by CLD2⁴ [⁴ https://github.com/CLD2Owners/cld2] and listed 100,000 large websites that roughly have the same size of English and Japanese texts. For this step, we used extractor⁵ [⁵ 5https://github.com/paracrawl/extractor] that was provided by the ParaCrawl project.