Unsupervised spectral learning of finite-state transducers
Document typeConference report
Rights accessOpen Access
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.
CitationBailly, R.; Carreras, X.; Quattoni, A.J. Unsupervised spectral learning of finite-state transducers. A: Neural Information Processing Systems Conference. "Advances in Neural Information Processing Systems 26". Lake Tahoe, Nevada: 2012, p. 800-808.
- GPLN - Grup de Processament del Llenguatge Natural - Ponències/Comunicacions de congressos 
- Departament de Ciències de la Computació - Ponències/Comunicacions de congressos [1.163]
- LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge - Ponències/Comunicacions de congressos 
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