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Title:
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Unsupervised spectral learning of finite-state transducers
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Author:
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Bailly, Raphaël; Carreras Pérez, Xavier; Quattoni, Ariadna Julieta
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
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Abstract:
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Finite-State Transducers (FST) are a standard tool for modeling paired inputoutput
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. |
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Abstract:
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Peer Reviewed |
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Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
-Finite-state transducers
-Finite State Transducers
Spectral Learning
-Transductors d'estats finits |
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Rights:
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Document type:
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Article - Submitted version
Conference Object |
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