Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers
This paper addresses the problem of unreliable evaluation practices in machine translation research, which can mislead the community and hinder progress, by providing a critical analysis and practical solutions.
The authors conducted a meta-evaluation of 769 machine translation papers from 2010-2020, revealing concerning trends such as reliance on BLEU scores without statistical testing or human evaluation, and proposed a guideline and scoring method to improve evaluation credibility.
This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our study shows that practices for automatic MT evaluation have dramatically changed during the past decade and follow concerning trends. An increasing number of MT evaluations exclusively rely on differences between BLEU scores to draw conclusions, without performing any kind of statistical significance testing nor human evaluation, while at least 108 metrics claiming to be better than BLEU have been proposed. MT evaluations in recent papers tend to copy and compare automatic metric scores from previous work to claim the superiority of a method or an algorithm without confirming neither exactly the same training, validating, and testing data have been used nor the metric scores are comparable. Furthermore, tools for reporting standardized metric scores are still far from being widely adopted by the MT community. After showing how the accumulation of these pitfalls leads to dubious evaluation, we propose a guideline to encourage better automatic MT evaluation along with a simple meta-evaluation scoring method to assess its credibility.