Transliteration Generation from the Orthographic and Phonetic Data

• Author / Creator

  Yao, Lei

• Machine transliteration is important to machine translation and cross-lingual information retrieval. Previous works show that machine transliteration can benefit from supplemental phonetic transcriptions and transliterations from other languages through a re-ranking framework. In this thesis, I propose to leverage supplemental information by jointly considering it with the spelling of the source word during the transliteration generation process. This approach is shown to be more accurate and faster than the re-ranking approach. In addition, I propose to represent supplemental transliterations through a common phonetic interlingua. Experiments suggest that the interlingua representations can be as effective as the original orthography, and can even be obtained from languages that are not available during training.

• Subjects / Keywords

  • Machine Transliteration
  • Interlingua
  • Joint Learning
  • Transliteration
  • NLP

• Graduation date

  Fall 2014

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3D795K1K

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.