We introduce the submissions of the NJUNLP team to the WMT 2023 Quality
Estimation (QE) shared task. Our team submitted predictions for the
English-German language pair on all two sub-tasks: (i) sentence- and word-level
quality prediction; and (ii) fine-grained error span detection. This year, we
further explore pseudo data methods for QE based on NJUQE framework
(this https URL). We generate pseudo MQM data using parallel
data from the WMT translation task. We pre-train the XLMR large model on pseudo
QE data, then fine-tune it on real QE data. At both stages, we jointly learn
sentence-level scores and word-level tags. Empirically, we conduct experiments
to find the key hyper-parameters that improve the performance. Technically, we
propose a simple method that covert the word-level outputs to fine-grained
error span results. Overall, our models achieved the best results in
English-German for both word-level and fine-grained error span detection
sub-tasks by a considerable margin.

我们介绍了 NJUNLP 团队在 WMT 2023 质量估计（QE）共享任务中的提交。我们的团队针对英德语言对，对两个子任务进行了预测:（i）句子和单词级质量预测；（ii）细粒度错误跨度检测。今年，我们在 NJUQE 框架上进一步探索了基于伪数据方法的 QE。我们使用 WMT 翻译任务的平行数据生成伪 MQM 数据。我们在伪 QE 数据上对 XLMR 大模型进行预训练，然后在真实 QE 数据上进行微调。在这两个阶段，我们同时学习句子级分数和单词级标签。通过实验证明，我们进行了实验以找到提高性能的关键超参数。在技术上，我们提出了一种简单的方法，将单词级输出转换为细粒度错误跨度结果。总的来说，我们的模型在英德语言对的单词级和细粒度错误跨度检测子任务中取得了最佳结果，并且差距明显。