Revisiting the Markov Property for Machine Translation

Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang
2/3/2024
cs.CL

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Abstract

In this paper, we re-examine the Markov property in the context of neural machine translation. We design a Markov Autoregressive Transformer~(MAT) and undertake a comprehensive assessment of its performance across four WMT benchmarks. Our findings indicate that MAT with an order larger than 4 can generate translations with quality on par with that of conventional autoregressive transformers. In addition, counter-intuitively, we also find that the advantages of utilizing a higher-order MAT do not specifically contribute to the translation of longer sentences.

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Cite this paper

@article{du2024revisiting,
  title  = {Revisiting the Markov Property for Machine Translation},
  author = {Cunxiao Du and Hao Zhou and Zhaopeng Tu and Jing Jiang},
  year   = {2024},
  eprint = {2402.02084},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.02084v1}
}

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