Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
Markus Freitag, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, Wolfgang Macherey Google Research {freitag, fosterg, grangier, vratnakar, qijuntan,
Abstract
Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions. While there has been considerable research on human evaluation, the field still lacks a commonly-accepted standard procedure. As a step toward this goal, we propose an evaluation methodology grounded in explicit error analysis, based on the Multidimensional Quality Metrics (MQM) framework. We carry out the largest MQM research study to date, scoring the outputs of top systems from the WMT 2020 shared task in two language pairs using annotations provided by professional translators with access to full document context. We analyze the resulting data extensively, finding among other results a substantially different ranking of evaluated systems from the one established by the WMT crowd workers, exhibiting a clear preference for human over machine output. Surprisingly, we also find that automatic metrics based on pre-trained embeddings can outperform human crowd workers. We make our corpus publicly available for further research.
中文速览
机器翻译质量越来越高，传统的众包打分方式已经难以准确区分顶尖系统之间的好坏，甚至会得出"机器翻译已达到人类水平"这样的误导性结论。为了解决这一问题，研究者采用多维质量指标（Multidimensional Quality Metrics，MQM）框架，让专业译者在完整文档语境下对错误进行逐一标注和分类，从而对 WMT 2020 评测中的顶尖翻译系统进行迄今最大规模的人工评估实验。结果发现，基于 MQM 的系统排名与原有众包评分排名差异显著——人工翻译明显优于机器翻译，而一些在众包评分中排名靠后的机器翻译系统排名大幅上升。更出人意料的是，基于预训练词向量的自动评估指标与 MQM 评分的相关性，竟然高于众包人工评分，说明在高质量翻译评测中，众包打分的可靠性甚至不如某些自动指标。这项研究公开了包含逾十万句段标注的语料库，为推动机器翻译评估走向更严谨的行业标准提供了重要依据。
原文 arXiv:2104.14478;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2104.14478v1