Abstract
Enterprises in the localization sector handle diverse content types, requiring precise localization solutions. Options range from raw machine translation to transcreation. But how can they ensure the best match between content and localization method? Traditionally, the decision relied mostly on human judgment. The PREDICT Methodology, crafted by Booking.com’s localization central team, offers a systematic framework for assessing MT suitability, aligning content type with the optimal localization solution. By integrating risk tolerance weights into binary queries about a source content and use case, PREDICT provides a score and recommended solution, from raw MT to human-only translation. This approach enables our business to provide the right quality for that specific content type, boost translation efficiency and reduce costs. Looking ahead, the methodology envisions integrating LLMs for automation and guidance, utilizing prompts to identify risk-mitigating strategies.- Anthology ID:
- 2024.amta-presentations.5
- Volume:
- Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations)
- Month:
- September
- Year:
- 2024
- Address:
- Chicago, USA
- Editors:
- Marianna Martindale, Janice Campbell, Konstantin Savenkov, Shivali Goel
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 45–74
- Language:
- URL:
- https://aclanthology.org/2024.amta-presentations.5
- DOI:
- Cite (ACL):
- Paula Manzur. 2024. PREDICT Methodology - Machine Translation Eligibility Criteria. In Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations), pages 45–74, Chicago, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- PREDICT Methodology - Machine Translation Eligibility Criteria (Manzur, AMTA 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.amta-presentations.5.pdf