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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.</abstract>
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%0 Conference Proceedings
%T Revisiting the Markov Property for Machine Translation
%A Du, Cunxiao
%A Zhou, Hao
%A Tu, Zhaopeng
%A Jiang, Jing
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F du-etal-2024-revisiting
%X 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.
%U https://aclanthology.org/2024.findings-eacl.40
%P 582-588

Markdown (Informal)

[Revisiting the Markov Property for Machine Translation](https://aclanthology.org/2024.findings-eacl.40) (Du et al., Findings 2024)

• Revisiting the Markov Property for Machine Translation (Du et al., Findings 2024)

ACL

• Cunxiao Du, Hao Zhou, Zhaopeng Tu, and Jing Jiang. 2024. Revisiting the Markov Property for Machine Translation. In Findings of the Association for Computational Linguistics: EACL 2024, pages 582–588, St. Julian’s, Malta. Association for Computational Linguistics.