The year 2024 marks the 10th anniversary of the Multidimensional Quality
Metrics (MQM) framework for analytic translation quality evaluation. The MQM
error typology has been widely used by practitioners in the translation and
localization industry and has served as the basis for many derivative projects.
The annual Conference on Machine Translation (WMT) shared tasks on both human
and automatic translation quality evaluations used the MQM error typology.
The metric stands on two pillars: error typology and the scoring model. The
scoring model calculates the quality score from annotation data, detailing how
to convert error type and severity counts into numeric scores to determine if
the content meets specifications. Previously, only the raw scoring model had
been published. This April, the MQM Council published the Linear Calibrated
Scoring Model, officially presented herein, along with the Non-Linear Scoring
Model, which had not been published before.
This paper details the latest MQM developments and presents a universal
approach to translation quality measurement across three sample size ranges. It
also explains why Statistical Quality Control should be used for very small
sample sizes, starting from a single sentence.

该论文详细介绍了最新的 MQM 发展，并提出了一种适用于三种样本大小范围的通用翻译质量测量方法，还介绍了为非常小的样本大小采用统计质量控制的原因，起点为单个句子。

翻译质量测量的多范围理论：MQM 评分模型和统计质量控制

The Multi-Range Theory of Translation Quality Measurement: MQM scoring  models and Statistical Quality Control

Translation quality evaluation plays a crucial role in machine translation.
According to the input format, it is mainly separated into three tasks, i.e.,
reference-only, source-only and source-reference-combined. Recent methods,
despite their promising results, are specifically designed and optimized on one
of them. This limits the convenience of these methods, and overlooks the
commonalities among tasks. In this paper, we propose UniTE, which is the first
unified framework engaged with abilities to handle all three evaluation tasks.
Concretely, we propose monotonic regional attention to control the interaction
among input segments, and unified pretraining to better adapt multi-task
learning. We testify our framework on WMT 2019 Metrics and WMT 2020 Quality
Estimation benchmarks. Extensive analyses show that our \textit{single model}
can universally surpass various state-of-the-art or winner methods across
tasks. Both source code and associated models are available at
this https URL.

本文提出了 UniTE 框架，通过引入单调区域注意力和统一预训练策略，该框架具有处理机器翻译中三个质量评估任务（即只有参考文献、只有源语言和源 - 参考文献组合）的能力，并在 WMT 2019 Metrics 和 WMT 2020 Quality Estimation 基准测试中表现出显著的泛化能力。