Abstract
When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.- Anthology ID:
- W19-1424
- Volume:
- Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
- Month:
- June
- Year:
- 2019
- Address:
- Ann Arbor, Michigan
- Editors:
- Marcos Zampieri, Preslav Nakov, Shervin Malmasi, Nikola Ljubešić, Jörg Tiedemann, Ahmed Ali
- Venue:
- VarDial
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 214–222
- Language:
- URL:
- https://aclanthology.org/W19-1424
- DOI:
- 10.18653/v1/W19-1424
- Cite (ACL):
- Pamela Shapiro and Kevin Duh. 2019. Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 214–222, Ann Arbor, Michigan. Association for Computational Linguistics.
- Cite (Informal):
- Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation (Shapiro & Duh, VarDial 2019)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/W19-1424.pdf