Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation
Bram Bulté, Arda Tezcan · 2019
We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM).We propose and test two methods for augmenting NMT training data with fuzzy TM matches.Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems.The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation.