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Despite the fact that multilingual agreement (MA) has shown its importance
for multilingual neural machine translation (MNMT), current methodologies in
the field have two shortages: (i) require parallel data between multiple
language pairs, which is not always realistic and (ii) optimize the agreement
in an ambiguous direction, which hampers the translation performance. We
present \textbf{B}idirectional \textbf{M}ultilingual \textbf{A}greement via
\textbf{S}witched \textbf{B}ack-\textbf{t}ranslation (\textbf{BMA-SBT}), a
novel and universal multilingual agreement framework for fine-tuning
pre-trained MNMT models, which (i) exempts the need for aforementioned parallel
data by using a novel method called switched BT that creates synthetic text
written in another source language using the translation target and (ii)
optimizes the agreement bidirectionally with the Kullback-Leibler Divergence
loss. Experiments indicate that BMA-SBT clearly improves the strong baselines
on the task of MNMT with three benchmarks: TED Talks, News, and Europarl.
In-depth analyzes indicate that BMA-SBT brings additive improvements to the
conventional BT method.
