On the Evaluation of Machine Translation for Terminology Consistency

• URL: http://arxiv.org/abs/2106.11891v1
• Date: Tue, 22 Jun 2021 15:59:32 GMT
• Title: On the Evaluation of Machine Translation for Terminology Consistency
• Authors: Md Mahfuz ibn Alam, Antonios Anastasopoulos, Laurent Besacier, James Cross, Matthias Gall\'e, Philipp Koehn, Vassilina Nikoulina
• Abstract summary: We propose metrics to measure the consistency of MT output with regards to a domain terminology. We perform studies on the COVID-19 domain over 5 languages, also performing terminology-targeted human evaluation.
• Score: 31.67296249688388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As neural machine translation (NMT) systems become an important part of professional translator pipelines, a growing body of work focuses on combining NMT with terminologies. In many scenarios and particularly in cases of domain adaptation, one expects the MT output to adhere to the constraints provided by a terminology. In this work, we propose metrics to measure the consistency of MT output with regards to a domain terminology. We perform studies on the COVID-19 domain over 5 languages, also performing terminology-targeted human evaluation. We open-source the code for computing all proposed metrics: https://github.com/mahfuzibnalam/terminology_evaluation

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