EntityRank: Unsupervised Mining of Bilingual Named Entity Pairs from Parallel Corpora for Neural Machine Translation
M. Zhang, Song Peng, Hao Yang, Yanqing Zhao, Xiaosong Qiao, Junhao Zhu, Shimin Tao, Ying Hua Qin, Yanfei Jiang · 2022 IEEE International Conference on Big Data (Big Data) · 2022
As Neural Machine Translation (NMT) heavily relies on training data, finding an effective method to help NMT make better use of limited data is of great significance. In this paper, with the motivation of the famous Google’s PageRank algorithm, we propose a novel unsupervised method EntityRank for mining bilingual named entity pairs from parallel corpora, which involves three critical components (Generator, Scorer and Filter). To apply the pairs mined by EntityRank to NMT, we design a data augmentation strategy for the state-of-the-art (SOTA) model Transformer. From the experimental results on the CCMT20 English-Chinese and WMT14 English-German news parallel corpora, it can be seen that the unsupervised method EntityRank could obtain relatively high quality bilingual named entity pairs; and with the designed data augmentation strategy, the mined pairs could not only significantly improve the translation quality of their covered data, but also benefit the translation quality of the overall data.