Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort

• URL: http://arxiv.org/abs/2105.13385v1
• Date: Thu, 27 May 2021 18:19:39 GMT
• Title: Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort
• Authors: V\^ania Mendon\c{c}a (1 and 2), Ricardo Rei (1 and 2 and 3), Luisa Coheur (1 and 2), Alberto Sardinha (1 and 2), Ana L\'ucia Santos (4 and 5) ((1) INESC-ID Lisboa, (2) Instituto Superior T\'ecnico, (3) Unbabel AI, (4) Centro de Lingu\'istica da Universidade de Lisboa, (5) Faculdade de Letras da Universidade de Lisboa)
• Abstract summary: In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. We propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems. Our experiments on WMT'19 datasets show that our online approach quickly converges to the top-3 ranked systems for the language pairs considered.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. Automatic metrics are not reliable when it comes to high performing systems. In addition, resorting to human evaluators can be expensive, especially when evaluating multiple systems. To overcome the latter challenge, we propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems, by taking advantage of the human feedback available. Our experiments on WMT'19 datasets show that our online approach quickly converges to the top-3 ranked systems for the language pairs considered, despite the lack of human feedback for many translations.

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