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

• URL: http://arxiv.org/abs/2210.09683v1
• Date: Tue, 18 Oct 2022 08:51:25 GMT
• Title: Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task
• Authors: Yu Wan, Keqin Bao, Dayiheng Liu, Baosong Yang, Derek F. Wong, Lidia S. Chao, Wenqiang Lei, Jun Xie
• Abstract summary: We build our system based on the core idea of UNITE (Unified Translation Evaluation) During the model pre-training phase, we first apply the pseudo-labeled data examples to continuously pre-train UNITE. During the fine-tuning phase, we use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data from past years' WMT competitions.
• Score: 61.34108034582074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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