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- Title
Learning finite-state models for machine translation.
- Authors
Francisco Casacuberta; Enrique Vidal
- Abstract
<div class="abstract"><a name="abs1"/><span class="abstractheading">Abstract??</span>In formal language theory, finite-state transducers are well-know models for simple ?input-output? mappings between two languages. Even if more powerful, recursive models can be used to account for more complex mappings, it has been argued that the input-output relations underlying most usual natural language pairs can essentially be modeled by finite-state devices. Moreover, the relative simplicity of these mappings has recently led to the development of techniques for learning finite-state transducers from a training set of input-output sentence pairs of the languages considered. In the last years, these techniques have lead to the development of a number of machine translation systems. Under the statistical statement of machine translation, we overview here how modeling, learning and search problems can be solved by using stochastic finite-state transducers. We also review the results achieved by the systems we have developed under this paradigm. As a main conclusion of this review we argue that, as task complexity and training data scarcity increase, those systems which rely more on statistical techniques tend produce the best results.</div>
- Subjects
LEARNING; MACHINE theory; ALGORITHMS; TRANSDUCERS
- Publication
Machine Learning, 2007, Vol 66, Issue 1, p69
- ISSN
0885-6125
- Publication type
Article