In this report, we present our submission to the WMT 2022 Metrics Shared
Task. We build our system based on the core idea of UNITE (Unified Translation
Evaluation), which unifies source-only, reference-only, and
source-reference-combined evaluation scenarios into one single model.
Specifically, during the model pre-training phase, we first apply the
pseudo-labeled data examples to continuously pre-train UNITE. Notably, to
reduce the gap between pre-training and fine-tuning, we use data cropping and a
ranking-based score normalization strategy. During the fine-tuning phase, we
use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data
from past years' WMT competitions. Specially, we collect the results from
models with different pre-trained language model backbones, and use different
ensembling strategies for involved translation directions.

该论文基于 UNITE（统一翻译评估）的核心理念，将源 - 参考 - 结合评估场景统一为一个模型，在使用不同的预训练模型背骨和不同的集成策略时，使用过去 WMT 比赛的直接评估和多维质量度量数据完成了模型预训练和微调。

阿里巴巴 - 翻译中国提交 WMT 2022 度量共享任务

Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task

Neural machine translation represents an exciting leap forward in translation
quality. But what longstanding weaknesses does it resolve, and which remain? We
address these questions with a challenge set approach to translation evaluation
and error analysis. A challenge set consists of a small set of sentences, each
hand-designed to probe a system's capacity to bridge a particular structural
divergence between languages. To exemplify this approach, we present an
English-French challenge set, and use it to analyze phrase-based and neural
systems. The resulting analysis provides not only a more fine-grained picture
of the strengths of neural systems, but also insight into which linguistic
phenomena remain out of reach.

本文采用挑战集方法对神经机器翻译进行评估和错误分析，提供了细粒度的神经系统强度分析以及哪些语言现象无法解决。