Evaluation briefs: drawing on translation studies for human evaluation of MT

In this position paper, we examine ways in which researchers in machine translation and translation studies have approached the problem of evaluating the output of machine translation systems and, more broadly, the questions of what it means to define translation quality. We explore their similarities and differences, highlighting the role that the purpose and context of translation plays in translation studies approaches. We argue that evaluation of machine translation (e.g., in shared tasks) would benefit from additional insights from translation studies, and we suggest the introduction of an ‘evaluation brief” (analogous to the ‘translation brief’) which could help set out useful context for annotators tasked with evaluating machine translation.

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Publication Details

Journal
NPARC
Published
2024-09-01
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
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article

Evaluation briefs: drawing on translation studies for human evaluation of MT

Elizabeth Marshman, Ting Liu, Rebecca Knowles, Chi-kiu Lo
NPARC
Natural Language Processing Techniques
article

Evaluation briefs: drawing on translation studies for human evaluation of MT

Elizabeth Marshman, Ting Liu, Rebecca Knowles, Chi-kiu Lo
article en

Abstract

In this position paper, we examine ways in which researchers in machine translation and translation studies have approached the problem of evaluating the output of machine translation systems and, more broadly, the questions of what it means to define translation quality. We explore their similarities and differences, highlighting the role that the purpose and context of translation plays in translation studies approaches. We argue that evaluation of machine translation (e.g., in shared tasks) would benefit from additional insights from translation studies, and we suggest the introduction of an ‘evaluation brief” (analogous to the ‘translation brief’) which could help set out useful context for annotators tasked with evaluating machine translation.

NPARC
Quality Education
Openalex Percentile: Top 40%
Natural Language Processing Techniques
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