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Study compares DeepL, eTranslation, Systran MT systems for specialized French translation

By PulseAugur Editorial · [1 sources] ·

A new study evaluates the performance of three machine translation (MT) systems—DeepL, eTranslation, and Systran—in translating specialized English content into French. The research also compared the post-editing efforts of two distinct groups: professional linguists/translators and Natural Language Processing (NLP) experts. Findings indicate notable differences in translation quality, particularly concerning terminology and fluency, across both the MT systems and the post-editor groups. The study underscores the critical role of domain-specific knowledge in specialized translation and points out the variable effectiveness of MT systems for language used in specific professional contexts. AI

IMPACT This research highlights the varying performance of MT systems in specialized domains, suggesting a continued need for human expertise in professional translation contexts.

RANK_REASON The cluster contains an academic paper detailing research findings on machine translation systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Study compares DeepL, eTranslation, Systran MT systems for specialized French translation

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The cluster contains an academic paper detailing research findings on machine translation systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Natalie Kübler ·

    Machine Translation and Post-Editing: Comparative Evaluation of Different MT Systems and Post-Editor Groups in Specialised Translation

    This article aims to evaluate the quality of machine translation (MT) and post-editing (PE) in the context of specialised translation from English into French. Three MT systems (DeepL, eTranslation and Systran) were compared, and two groups of post-editors -linguists/translators …

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