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Unsupervised Spectral Learning of Finite State Transducers
Raphael Bailly · Xavier Carreras · Ariadna Quattoni

Fri Dec 06 10:26 AM -- 10:30 AM (PST) @ Harvey's Convention Center Floor, CC

Finite-State Transducers (FST) are a standard tool for modeling paired input-output sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. presented a spectral algorithm for learning FST from samples of aligned input-output sequences. In this paper we address the more realistic, yet challenging setting where the alignments are unknown to the learning algorithm. We frame FST learning as finding a low rank Hankel matrix satisfying constraints derived from observable statistics. Under this formulation, we provide identifiability results for FST distributions. Then, following previous work on rank minimization, we propose a regularized convex relaxation of this objective which is based on minimizing a nuclear norm penalty subject to linear constraints and can be solved efficiently.

Author Information

Raphael Bailly (Universitat Polit├Ęcnica de Catalunya)
Xavier Carreras (Universitat Polit├Ęcnica de Catalunya)
Ariadna Quattoni (Xerox Research Centre Europe)

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