The MAKE-NMTVIZ System Description for the WMT23 Literary Task
Fabien Lopez, Gabriela González, Damien Hansen, Mariam Nakhle, Behnoosh Namdarzadeh, Nicolas Ballier, Marco Dinarelli, Emmanuelle Esperança-Rodier, Sui He, Sadaf Mohseni, Caroline Rossi, Didier Schwab, Jun Yang, Jean-Baptiste Yunès, Lichao Zhu
Abstract
This paper describes the MAKE-NMTVIZ Systems trained for the WMT 2023 Literary task. As a primary submission, we used Train, Valid1, test1 as part of the GuoFeng corpus (Wang et al., 2023) to fine-tune the mBART50 model with Chinese-English data. We followed very similar training parameters to (Lee et al. 2022) when fine-tuning mBART50. We trained for 3 epochs, using gelu as an activation function, with a learning rate of 0.05, dropout of 0.1 and a batch size of 16. We decoded using a beam search of size 5. For our contrastive1 submission, we implemented a fine-tuned concatenation transformer (Lupo et al., 2023). The training was developed in two steps: (i) a sentence-level transformer was implemented for 10 epochs trained using general, test1, and valid1 data (more details in contrastive2 system); (ii) second, we fine-tuned at document-level using 3-sentence concatenation for 4 epochs using train, test2, and valid2 data. During the fine-tuning, we used ReLU as an activation function, with an inverse square root learning rate, dropout of 0.1, and a batch size of 64. We decoded using a beam search of size. Four our contrastive2 and last submission, we implemented a sentence-level transformer model (Vaswani et al., 2017). The model was trained with general data for 10 epochs using general-purpose, test1, and valid 1 data. The training parameters were an inverse square root scheduled learning rate, a dropout of 0.1, and a batch size of 64. We decoded using a beam search of size 4. We then compared the three translation outputs from an interdisciplinary perspective, investigating some of the effects of sentence- vs document-based training. Computer scientists, translators and corpus linguists discussed the linguistic remaining issues for this discourse-level literary translation.- Anthology ID:
- 2023.wmt-1.30
- 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:
- 287–295
- Language:
- URL:
- https://aclanthology.org/2023.wmt-1.30
- DOI:
- 10.18653/v1/2023.wmt-1.30
- Cite (ACL):
- Fabien Lopez, Gabriela González, Damien Hansen, Mariam Nakhle, Behnoosh Namdarzadeh, Nicolas Ballier, Marco Dinarelli, Emmanuelle Esperança-Rodier, Sui He, Sadaf Mohseni, Caroline Rossi, Didier Schwab, Jun Yang, Jean-Baptiste Yunès, and Lichao Zhu. 2023. The MAKE-NMTVIZ System Description for the WMT23 Literary Task. In Proceedings of the Eighth Conference on Machine Translation, pages 287–295, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- The MAKE-NMTVIZ System Description for the WMT23 Literary Task (Lopez et al., WMT 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.wmt-1.30.pdf