Differential Translation for Japanese Partially Amended Statutory Sentences

Evaluation Scheme of Focal Translation for Japanese Partially Amended Statutes

“…Third, they use word alignment to find English expressions that correspond to Japanese ones, perhaps weakening their performance due to alignment error. Yamakoshi et al (2020)'s method solved these problems by incorporating NMT with a templateaware SMT. Their method, which uses an NMT model and a template-aware SMT model, allows the former to output n-best translations as candidates by applying Monte Carlo (MC) dropout (Gal and Ghahramani, 2016) to improve the output diversity.…”

“…However, both metrics are indifferent to whether an expression in the system output is a changed part in the amendment, and thus both fail to indicate the quality of the focality. Yamakoshi et al (2020) proposed focality scores to solve this issue. A focality score quantizes the focality of the system output by calculating the recall of the n-grams shared by both the pre-and post-amendment translations.…”

“…To solve the problem discussed in the previous section, our study focuses on translating partially amended statutes automatically. More specifically, it adopts a task declared by Yamakoshi et al (2020). Among the categories described in the previous section, the task focuses on categories that modify the parts of an existing statutory sentence (i.e., category 1).…”

“…However, as of January 2020, only 23.4% (163/697) of the translated statutes in JLT correspond to their latest versions (Yamakoshi et al, 2020). After amending a statute, its translation must be promptly updated to avoid creating confusion among international readers.…”

“…describing the facts of the marine accident" is fluent and adequate, it is unsuitable as a revision from the focality perspective because "申立て" (moshitate; request) and "示し て" (shimeshite; stating), which are irrelevant to the amendment, were changed. Yamakoshi et al (2020) proposed a machine translation method for Japanese partially amendment translation that generates translation candidates by a Transformer (Vaswani et al, 2017)based neural machine translation (NMT) model. It selects the best one by comparing the candidates with the output of a template-aware statistical machine translation (SMT) model (e.g., (Koehn and Senellart, 2010;Kozakai et al, 2017)) that only changes the affected expressions.…”