An analysis of Google Translate and DeepL translation of source text typographical errors in the economic and legal fields

Training neural machine translation systems with noisy data has been shown to improve robustness (Heigold et al., 2018). The objective of the present study is to test Google Translate and DeepL performance in the detection and correction of typographical errors, by introducing 1,820 source text typo...

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Bibliographic Details

Published in:	Revista de llengua i dret pp. 88 - 105
Main Author:	Santiago Rodríguez-Rubio Mediavilla
Format:	Journal Article
Language:	Aragonese Spanish English
Published:	Escola d'Administració Pública de Catalunya 01-06-2024
Subjects:	deepl google translate neural machine translation specialised texts translation typographical errors
Online Access:	Get full text
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