Abstract
We present and empirically compare a range of novel probabilistic finite-state transducer (PFST) models targeted at two major natural language string transduction tasks, transliteration selection and cognate translation selection. Evaluation is performed on 10 distinct language pair data sets, and in each case novel models consistently and substantially outperform a well-established standard reference algorithm.- Anthology ID:
- 2006.amta-papers.23
- Volume:
- Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers
- Month:
- August 8-12
- Year:
- 2006
- Address:
- Cambridge, Massachusetts, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 203–212
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2006.amta-papers.23/
- DOI:
- Cite (ACL):
- Charles Schafer. 2006. Novel Probabilistic Finite-State Transducers for Cognate and Transliteration Modeling. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 203–212, Cambridge, Massachusetts, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Novel Probabilistic Finite-State Transducers for Cognate and Transliteration Modeling (Schafer, AMTA 2006)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2006.amta-papers.23.pdf