Non-Fluent Synthetic Target-Language Data Improve Neural Machine
  Translation
Víctor M. Sánchez-Cartagena Miquel Esplà-Gomis Juan Antonio Pérez-Ortiz Felipe Sánchez-Martínez
When the amount of parallel sentences available to train a neural machine
translation is scarce, a common practice is to generate new synthetic training
samples from them. A number of approaches have been proposed to produce
synthetic parallel sentences that are similar to those in the parallel data
available. These approaches work under the assumption that non-fluent
target-side synthetic training samples can be harmful and may deteriorate
translation performance. Even so, in this paper we demonstrate that synthetic
training samples with non-fluent target sentences can improve translation
performance if they are used in a multilingual machine translation framework as
if they were sentences in another language. We conducted experiments on ten
low-resource and four high-resource translation tasks and found out that this
simple approach consistently improves translation performance as compared to
state-of-the-art methods for generating synthetic training samples similar to
those found in corpora. Furthermore, this improvement is independent of the
size of the original training corpus, the resulting systems are much more
robust against domain shift and produce less hallucinations.