HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task
Hao Yang, Minghan Wang, Daimeng Wei, Hengchao Shang, Jiaxin Guo, Zongyao Li, Lizhi Lei, Ying Qin, Shimin Tao, Shiliang Sun, Yimeng Chen
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The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottleneck Adapter Layers are integrated into the model to prevent over-fitting. We further collect external translations as the augmented MT candidates to improve the performance. The experiment demonstrates that pre-trained NMT models are effective when fine-tuning with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our system achieves competitive results on both directions in the final evaluation.