Tag Assisted Neural Machine Translation of Film Subtitles
Aren Siekmeier, WonKee Lee, Hong-Seok Kwon, Jong-Hyeok Lee · 2021
We implemented a neural machine translation system that uses automatic sequence tagging to improve the quality of translation.Instead of operating on unannotated sentence pairs, our system uses pre-trained tagging systems to add linguistic features to source and target sentences.Our proposed neural architecture learns a combined embedding of tokens and tags in the encoder, and simultaneous token and tag prediction in the decoder.Compared to a baseline with unannotated training, this architecture increased the BLEU score of German to English film subtitle translation outputs by 1.61 points using named entity tags; however, the BLEU score decreased by 0.38 points using part-of-speech tags.This demonstrates that certain token-level tag outputs from off-theshelf tagging systems can improve the output of neural translation systems using our combined embedding and simultaneous decoding extensions.