@inproceedings{an-etal-2023-max,
    title = "{MAX}-{ISI} System at {WMT}23 Discourse-Level Literary Translation Task",
    author = "An, Li  and
      Jin, Linghao  and
      Ma, Xuezhe",
    editor = "Koehn, Philipp  and
      Haddow, Barry  and
      Kocmi, Tom  and
      Monz, Christof",
    booktitle = "Proceedings of the Eighth Conference on Machine Translation",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2023.wmt-1.29/",
    doi = "10.18653/v1/2023.wmt-1.29",
    pages = "282--286",
    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."
}