- Yaoming Zhu, Jiangtao Feng, Chengqi Zhao, Mingxuan Wang, Lei Li. 2021. Serial or Parallel? Plug-able Adapter for multilingual machine translation. Abstract: 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 defu-sion 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 PAM outperforms several strong competitors, including series adapter and multilingual knowledge distillation.
