Serial or Parallel? Plug-able Adapter for multilingual machine translation

• URL: http://arxiv.org/abs/2104.08154v1
• Date: Fri, 16 Apr 2021 14:58:28 GMT
• Title: Serial or Parallel? Plug-able Adapter for multilingual machine translation
• Authors: Yaoming Zhu, Jiangtao Feng, Chengqi Zhao, Mingxuan Wang, Lei Li
• Abstract summary: We propose PAM, a Transformer model augmented with defusion adaptation for multilingual machine translation. PAM consists of embedding and layer adapters to shift the word and intermediate representations towards language-specific ones. Experiment results on IWSLT, OPUS-100, and WMT benchmarks show that method outperforms several strong competitors.
• Score: 15.114588783601466
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

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