Extending Multilingual Machine Translation through Imitation Learning
Wen Lai Viktor Hangya Alexander Fraser
Despite the growing variety of languages supported by existing multilingual
neural machine translation (MNMT) models, most of the world's languages are
still being left behind. We aim to extend large-scale MNMT models to a new
language, allowing for translation between the newly added and all of the
already supported languages in a challenging scenario: using only a parallel
corpus between the new language and English. Previous approaches, such as
continued training on parallel data including the new language, suffer from
catastrophic forgetting (i.e., performance on other languages is reduced). Our
novel approach Imit-MNMT treats the task as an imitation learning process,
which mimicks the behavior of an expert, a technique widely used in the
computer vision area, but not well explored in NLP. More specifically, we
construct a pseudo multi-parallel corpus of the new and the original languages
by pivoting through English, and imitate the output distribution of the
original MNMT model. Extensive experiments show that our approach significantly
improves the translation performance between the new and the original
languages, without severe catastrophic forgetting. We also demonstrate that our
approach is capable of solving copy and off-target problems, which are two
common issues existence in current large-scale MNMT models.