A

Multi-Agent Mutual Learning at Sentence-Level and Token-Level for Neural Machine Translation

Abstract

Mutual learning, where multiple agents learn collaboratively and teach one another, has been shown to be an effective way to distill knowledge for image classification tasks.In this paper, we extend mutual learning to the machine translation task and operate at both the sentence-level and the token-level.Firstly, we co-train multiple agents by using the same parallel corpora.After convergence, each agent selects and learns its poorly predicted tokens from other agents.The poorly predicted tokens are determined by the acceptance-rejection sampling algorithm.Our experiments show that sequential mutual learning at the sentence-level and the token-level improves the results cumulatively.Absolute improvements compared to strong baselines are obtained on various translation tasks.On the IWSLT'14 German-English task, we get a new state-of-the-art BLEU score of 37.0.We also report a competitive result, 29.9 BLEU score, on the WMT'14 English-German task.

Authors 3

  1. RWTH Aachen University

    Affiliation as printed

    Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University D-52056 Aachen , Germany

    Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University D-52056 Aachen, Germany

  2. RWTH Aachen University

    Affiliation as printed

    Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University D-52056 Aachen , Germany

    Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University D-52056 Aachen, Germany

  3. RWTH Aachen University

    Affiliation as printed

    Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University D-52056 Aachen , Germany

    Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University D-52056 Aachen, Germany

Cited by 5 stored of 5

5 results

No patents citing this paper on Lens.org (checked 2026-10-06).

References 33