Open AccessOn Post-Editability of Machine Translated Texts
Author(s) -
Ch Ram Anirudh,
K. N. Balasubramanya Murthy
Publication year - 2021
Publication title -
translation today
Language(s) - English
Resource type - Journals
ISSN - 0972-8740
DOI - 10.46623/tt/2021.15.1.ar4
Subject(s) - machine translation , computer science , phrase , task (project management) , natural language processing , evaluation of machine translation , artificial intelligence , bleu , hindi , quality (philosophy) , translation (biology) , machine learning , example based machine translation , machine translation software usability , engineering , philosophy , biochemistry , chemistry , systems engineering , epistemology , messenger rna , gene
Machine Translated texts are often far from perfect and postediting is essential to get publishable quality. Post-editing may not always be a pleasant task. However, modern machine translation (MT) approaches like Statistical MT (SMT) and Neural MT (NMT) seem to hold greater promise. In this work, we present a quantitative method for scoring translations and computing the post-editability of MT system outputs.We show that the scores we get correlate well with MT evaluation metrics as also with the actual time and effort required for post-editing. We compare the outputs of three modern MT systems namely phrase-based SMT (PBMT), NMT, and Google translate for their Post-Editability for English to Hindi translation. Further, we explore the effect of various kinds of errors in MT outputs on postediting time and effort. Including an Indian language in this kind of post-editability study and analyzing the influence oferrors on postediting time and effort for NMT are highlights of this work.