PEACook: Post-editing Advancement Cookbook

Shimin Tao, Jiaxing Guo, Yanqing Zhao, Min Zhang, Daimeng Wei, Minghan Wang, Hao Yang, Miaomiao Ma, Ying Qin

Abstract: Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing efforts. Training on APE models has made a great progress since 2015; however, whether APE models are really performing well on domain samples remains as an open question, and achieving this is still a hard task. This paper provides a mobile domain APE corpus with 50.1 TER/37.4 BLEU for the En-Zh language pair. This corpus is much more practical than that provided in WMT 2021 APE tasks (18.05 TER/71.07 BLEU for En-De, 22.73 TER/69.2 BLEU for En-Zh) [1]. To obtain a more comprehensive investigation on the presented corpus, this paper provides two mainstream models as the Cookbook baselines: (1) Autoregressive Translation APE model (AR-APE) based on HW-TSC APE 2020 [2], which is the SOTA model of WMT 2020 APE tasks. (2) Non-Autoregressive Translation APE model (NAR-APE) based on the well-known Levenshtein Transformer [3]. Experiments show that both the mainstream models of AR and NAR can effectively improve the effect of APE. The corpus has been released in the CCMT 2022 APE evaluation task and the baseline models will be open-sourced.