English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs
Masahiro Kaneko, Aizhan Imankulova, Tosho Hirasawa, Mamoru Komachi · 2020
We introduce our TMU system that is submitted to The 4th Workshop on Neural Generation and Translation (WNGT2020) to Englishto-Japanese (En→Ja) track on Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task.In most cases machine translation systems generate a single output from the input sentence, however, in order to assist language learners in their journey with better and more diverse feedback, it is helpful to create a machine translation system that is able to produce diverse translations of each input sentence.However, creating such systems would require complex modifications in a model to ensure the diversity of outputs.In this paper, we investigated if it is possible to create such systems in a simple way and whether it can produce desired diverse outputs.In particular, we combined the outputs from forward and backward neural translation models (NMT).Our system achieved third place in En→Ja track, despite adopting only a simple approach.