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Article
MT for Minority Languages Using Elicitation-Based Learning of Syntactic Transfer Rules
Computer Science Department
  • Katharina Probst, Carnegie Mellon University
  • Lori Levin, Carnegie Mellon University
  • Erik Peterson, Carnegie Mellon University
  • Alon Lavie, Carnegie Mellon University
  • Jaime G. Carbonell, Carnegie Mellon University
Date of Original Version
8-1-2003
Type
Article
Abstract or Description
The AVENUE project contains a run-time machine translation program that is surrounded by pre- and post-run-time modules. The post-run-time module selects among translation alternatives. The pre-run-time modules are concerned with elicitation of data and automatic learning of transfer rules in order to facilitate the development of machine translation between a language with extensive resources for natural language processing and a language with few resources for natural language processing. This paper describes the run-time transfer-based machine translation system as well as two of the pre-run-time modules: elicitation of data from the minority language and automated learning of transfer rules from the elicited data.
DOI
10.1023/B:COAT.0000021003.55041.fd
Citation Information
Katharina Probst, Lori Levin, Erik Peterson, Alon Lavie, et al.. "MT for Minority Languages Using Elicitation-Based Learning of Syntactic Transfer Rules" (2003)
Available at: http://works.bepress.com/jaime_carbonell/2/