Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021
Katsuki Chousa, Makoto Morishita · 2021
This paper describes our systems that were submitted to the restricted translation task at WAT 2021.In this task, the systems are required to output translated sentences that contain all given word constraints.Our system combined input augmentation and constrained beam search algorithms.Through experiments, we found that this combination significantly improves translation accuracy and can save inference time while containing all the constraints in the output.For both En→Ja and Ja→En, our systems obtained the best translation performances in both automatic and human evaluations. * Equal contribution. 光線一致に基づく定常波の幾何光学的理論を展開した。