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        <title>MAX-ISI System at WMT23 Discourse-Level Literary Translation Task</title>
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            <title>Proceedings of the Eighth Conference on Machine Translation</title>
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    <abstract>This paper describes our translation systems for the WMT23 shared task. We participated in the discourse-level literary translation task - constrained track. In our methodology, we conduct a comparative analysis between the conventional Transformer model and the recently introduced MEGA model, which exhibits enhanced capabilities in modeling long-range sequences compared to the traditional Transformers. To explore whether language models can more effectively harness document-level context using paragraph-level data, we took the approach of aggregating sentences into paragraphs from the original literary dataset provided by the organizers. This paragraph-level data was utilized in both the Transformer and MEGA models. To ensure a fair comparison across all systems, we employed a sentence-alignment strategy to reverse our translation results from the paragraph-level back to the sentence-level alignment. Finally, our evaluation process encompassed sentence-level metrics such as BLEU, as well as two document-level metrics: d-BLEU and BlonDe.</abstract>
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%0 Conference Proceedings
%T MAX-ISI System at WMT23 Discourse-Level Literary Translation Task
%A An, Li
%A Jin, Linghao
%A Ma, Xuezhe
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F an-etal-2023-max
%X This paper describes our translation systems for the WMT23 shared task. We participated in the discourse-level literary translation task - constrained track. In our methodology, we conduct a comparative analysis between the conventional Transformer model and the recently introduced MEGA model, which exhibits enhanced capabilities in modeling long-range sequences compared to the traditional Transformers. To explore whether language models can more effectively harness document-level context using paragraph-level data, we took the approach of aggregating sentences into paragraphs from the original literary dataset provided by the organizers. This paragraph-level data was utilized in both the Transformer and MEGA models. To ensure a fair comparison across all systems, we employed a sentence-alignment strategy to reverse our translation results from the paragraph-level back to the sentence-level alignment. Finally, our evaluation process encompassed sentence-level metrics such as BLEU, as well as two document-level metrics: d-BLEU and BlonDe.
%R 10.18653/v1/2023.wmt-1.29
%U https://aclanthology.org/2023.wmt-1.29
%U https://doi.org/10.18653/v1/2023.wmt-1.29
%P 282-286

Markdown (Informal)

[MAX-ISI System at WMT23 Discourse-Level Literary Translation Task](https://aclanthology.org/2023.wmt-1.29) (An et al., WMT 2023)

• MAX-ISI System at WMT23 Discourse-Level Literary Translation Task (An et al., WMT 2023)

ACL

• Li An, Linghao Jin, and Xuezhe Ma. 2023. MAX-ISI System at WMT23 Discourse-Level Literary Translation Task. In Proceedings of the Eighth Conference on Machine Translation, pages 282–286, Singapore. Association for Computational Linguistics.