TMop: a Tool for Unsupervised Translation Memory Cleaning

Sabet, Masoud Jalili; Negri, Matteo; Turchi, Marco; Souza, José G. C. de; Federico, Marcello

doi:10.18653/v1/p16-4009

Cited by 3 publications

References 6 publications

(4 reference statements)

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The first Automatic Translation Memory Cleaning Shared Task

Barbu¹,

Escartín

Bentivogli³

et al. 2016

Machine Translation

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This paper reports on the organization and results of the rst Automatic\ud Translation Memory Cleaning Shared Task. This shared task is aimed\ud at nding automatic ways of cleaning translation memories (TMs) that have\ud not been properly curated and thus include incorrect translations. As a follow\ud up of the shared task, we also conducted two surveys, one targeting the teams\ud participating in the shared task, and the other one targeting professional translators.\ud While the researchers-oriented survey aimed at gathering information\ud about the opinion of participants on the shared task, the translators-oriented\ud survey aimed to better understand what constitutes a good TM unit and inform\ud decisions that will be taken in future editions of the task. In this paper, we\ud report on the process of data preparation and the evaluation of the automatic\ud systems submitted, as well as on the results of the collected surveys

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The first Automatic Translation Memory Cleaning Shared Task

Barbu¹,

Escartín

Bentivogli³

et al. 2016

Machine Translation

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Human or Neural Translation?

Bhardwaj¹,

Hermelo²,

Langlais

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Deep neural models tremendously improved machine translation. In this context, we investigate whether distinguishing machine from human translations is still feasible. We trained and applied 18 classifiers under two settings: a monolingual task, in which the classifier only looks at the (French) translation; and a bilingual task, in which the source text (in English) is also taken into consideration. We report on extensive experiments involving 4 neural MT systems (Google Translate, DeepL, as well as two systems we trained) and varying the domain of texts. We show that the bilingual task is the easiest one and that transfer-based deep-learning classifiers perform best, with mean accuracies around 85% in-domain and 75% out-of-domain.

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Familiarity of Iranian M.A. Translation Students with ICT Tools

Abdi¹

2021

IJLTS

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Familiarity with information and communication technology (ICT) is of great importance to the translation students because it allows the students to make use of a wide range of ICT tools. The present study investigated the degree of students’ familiarity with ICT tools employed to support ICT related activities included in the translator’s workstation. To do this, a questionnaire encompassing 24 questions was designed on the basis of translation activities proposed by Fulford and Granell-Zafar (2005), including information search and retrieval, communications, and marketing and work procurement. The results indicated the high familiarity of the M.A. translation students with general-purpose software application, namely online dictionaries and internet search engines, and the lower than the average familiarity of them with specific-purpose software, such as FTP and MUDs. Furthermore, chi-square test (X²) was run to see whether there is a significant relationship between each type of ICT tools and the participants. The results illustrated that the relationships between the M.A. translation students and some ICT applications, including internet search engines, web browsers, online dictionaries and encyclopedia, IRC, and MUDs, were significant; whereas, it was not significant between the other types of ICT software and students. This includes online translation marketplaces, internet forums, email, instant messaging, video chat, discussion mailing lists, talkers, and FTP.

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TMop: a Tool for Unsupervised Translation Memory Cleaning

Cited by 3 publications

References 6 publications

The first Automatic Translation Memory Cleaning Shared Task

The first Automatic Translation Memory Cleaning Shared Task

Human or Neural Translation?

Familiarity of Iranian M.A. Translation Students with ICT Tools

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