English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs

Masahiro Kaneko, Aizhan Imankulova, Tosho Hirasawa, Mamoru Komachi


Abstract
We introduce our TMU system that is submitted to The 4th Workshop on Neural Generation and Translation (WNGT2020) to English-to-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.
Anthology ID:
2020.ngt-1.15
Volume:
Proceedings of the Fourth Workshop on Neural Generation and Translation
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | NGT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
134–138
Language:
URL:
https://www.aclweb.org/anthology/2020.ngt-1.15
DOI:
10.18653/v1/2020.ngt-1.15
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ngt-1.15.pdf
Video:
 http://slideslive.com/38929829