  Developing a unified multilingual translation model is a key topic in machine
translation research. However, existing approaches suffer from performance
degradation: multilingual models yield inferior performance compared to the
ones trained separately on rich bilingual data. We attribute the performance
degradation to two issues: multilingual embedding conflation and multilingual
fusion effects. To address the two issues, we propose PAM, a Transformer model
augmented with defusion adaptation for multilingual machine translation.
Specifically, PAM consists of embedding and layer adapters to shift the word
and intermediate representations towards language-specific ones. Extensive
experiment results on IWSLT, OPUS-100, and WMT benchmarks show that \method
outperforms several strong competitors, including series adapter and
multilingual knowledge distillation.
