Evaluation Scheme of Focal Translation for Japanese Partially Amended Statutes
For updating the translations of Japanese statutes based on their amendments, we need to consider the translation “focality;” that is, we should only modify expressions that are relevant to the amendment and retain the others to avoid misconstruing its contents. In this paper, we introduce an evaluation metric and a corpus to improve focality evaluations. Our metric is called an Inclusive Score for DIfferential Translation: (ISDIT). ISDIT consists of two factors: (1) the n-gram recall of expressions unaffected by the amendment and (2) the n-gram precision of the output compared to the reference. This metric supersedes an existing one for focality by simultaneously calculating the translation quality of the changed expressions in addition to that of the unchanged expressions. We also newly compile a corpus for Japanese partially amendment translation that secures the focality of the post-amendment translations, while an existing evaluation corpus does not. With the metric and the corpus, we examine the performance of existing translation methods for Japanese partially amendment translations.
Code (0)
등록된 구현이 없습니다.
Tasks
TranslationSimilar Papers 제목 키워드 기반
Consistent Classification of Translation Revisions: A Case Study of English-Japanese Student Translations
Consistency is a crucial requirement in text annotation. It is especially important in educational applications, as lack of consistency directly affects learners{'} motivation and learning performance. This paper present…
Decision MakingGeneral Classificationtext annotationTranslationLinguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses
In the field of Japanese-Chinese translation linguistics, the issue of correctly translating attributive clauses has persistently proven to be challenging. Present-day machine translation tools often fail to accurately t…
Language ModelingLanguage ModellingLarge Language ModelMachine Translation+1Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language
The NAIST Lang-8 Learner Corpora (Lang-8 corpus) is one of the largest second-language learner corpora. The Lang-8 corpus is suitable as a training dataset for machine translation-based grammatical error correction syste…
Grammatical Error CorrectionMachine TranslationNMTTranslationWord Order in English-Japanese Simultaneous Interpretation: Analyses and Evaluation using Chunk-wise Monotonic Translation
This paper analyzes the features of monotonic translations, which follow the word order of the source language, in simultaneous interpreting (SI). Word order differences are one of the biggest challenges in SI, especiall…
TranslationEvaluation Dataset for Zero Pronoun in Japanese to English Translation
In natural language, we often omit some words that are easily understandable from the context. In particular, pronouns of subject, object, and possessive cases are often omitted in Japanese; these are known as zero prono…
Machine TranslationTranslation