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.- Anthology ID:
- 2023.wmt-1.29
- Volume:
- Proceedings of the Eighth Conference on Machine Translation
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 282–286
- Language:
- URL:
- https://aclanthology.org/2023.wmt-1.29
- DOI:
- 10.18653/v1/2023.wmt-1.29
- Cite (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.
- Cite (Informal):
- MAX-ISI System at WMT23 Discourse-Level Literary Translation Task (An et al., WMT 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.wmt-1.29.pdf