earticle

영어

There was a big technical progress in the research field of machine translation: the main approach has switched from statistical machine translation (SMT) to neural machine translation (NMT), leading to dramatic improvements in translation quality. Recently, another progress has been taking place from recurrent neural network(RNN)-based NMT to transformer-based NMT (T-NMT). As the performance of NMT has evolved, a lot of research papers for machine translation have been published in the field of interpretation and translation. Their main focus is on whether machine translation can replace human translation, and analyzing the quality of translation results. In this paper, we briefly explain the history of the machine translation research and review the mechanism of NMT. NMT is basically composed of three parts: encoder, attention mechanism, and decoder. Further we discuss the new transformer structure based on the encoder-decoder model. We also discuss the challenges in NMT and explain the research direction or solutions to the problems. Particular attention is given to the mistranslation of NMT, quality of the translation, and robustness against the noises in the training dataset as well as in the testing sentences. In order to test the performance of transformer-based NMT, we used the Google NMT (GNMT) service for 4 languages – Korean, English, German, and Japanese. We confirmed the robustness against sentences with noises. However, we found unexpected volatility of NMT models where the input sentence is semantically and syntactically correct, resulting in critical degradation of translation quality.