Should we find another model? Improving Neural Machine Translation Performance with ONE-Piece Tokenization Method without Model Modification

Most of the recent natural language processing (NLP) studies are based on the pretrain-finetuning approach (PFA). However for small and medium-sized industries with insufficient hardware, there are many limitations in servicing latest PFA based NLP application software, due to slow speed and insufficient memory. Since these approaches generally require large amounts of data, it is much more difficult to service with PFA especially for low-resource languages. We propose a new tokenization method, ONE-Piece, to address this limitation. ONE-Piece combines morphologically-aware subword tokenization and vocabulary communicating method, which has not been carefully considered before. Our proposed method can also be utilized without modifying the model structure. We experiment by applying ONE-Piece to Korean, a morphologically-rich and low-resource language. We revealed that ONE-Piece with vanilla transformer model can achieve comparable performance to the current Korean-English machine translation state-of-the-art model.