Hierarchy theorems for two-way finite state transducers

S.A. Greibach

doi:10.1007/bf00264603

Composition of weighted finite transducers in MapReduce

Journal Of Big Data ◽

10.1186/s40537-020-00397-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Bilal Elghadyry ◽

Faissal Ouardi ◽

Sébastien Verel

Keyword(s):

Speech Processing ◽

Large Scale ◽

Large Scale Data ◽

Finite State Transducers ◽

Wide Range ◽

Finite State ◽

Common Operation ◽

Efficient Representation ◽

Weighted Finite State Transducers ◽

Np Hardness

AbstractWeighted finite-state transducers have been shown to be a general and efficient representation in many applications such as text and speech processing, computational biology, and machine learning. The composition of weighted finite-state transducers constitutes a fundamental and common operation between these applications. The NP-hardness of the composition computation problem presents a challenge that leads us to devise efficient algorithms on a large scale when considering more than two transducers. This paper describes a parallel computation of weighted finite transducers composition in MapReduce framework. To the best of our knowledge, this paper is the first to tackle this task using MapReduce methods. First, we analyze the communication cost of this problem using Afrati et al. model. Then, we propose three MapReduce methods based respectively on input alphabet mapping, state mapping, and hybrid mapping. Finally, intensive experiments on a wide range of weighted finite-state transducers are conducted to compare the proposed methods and show their efficiency for large-scale data.

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FINITELY SUBSEQUENTIAL TRANSDUCERS

International Journal of Foundations of Computer Science ◽

10.1142/s0129054103002126 ◽

2003 ◽

Vol 14 (06) ◽

pp. 983-994 ◽

Cited By ~ 9

Author(s):

CYRIL ALLAUZEN ◽

MEHRYAR MOHRI

Keyword(s):

Speech Recognition ◽

Finite Number ◽

Efficient Algorithm ◽

Experimental Results ◽

Theoretical Formulation ◽

Large Vocabulary ◽

Finite State Transducers ◽

Finite State ◽

Large Vocabulary Speech Recognition

Finitely subsequential transducers are efficient finite-state transducers with a finite number of final outputs and are used in a variety of applications. Not all transducers admit equivalent finitely subsequential transducers however. We briefly describe an existing generalized determinization algorithm for finitely subsequential transducers and give the first characterization of finitely subsequentiable transducers, transducers that admit equivalent finitely subsequential transducers. Our characterization shows the existence of an efficient algorithm for testing finite subsequentiability. We have fully implemented the generalized determinization algorithm and the algorithm for testing finite subsequentiability. We report experimental results showing that these algorithms are practical in large-vocabulary speech recognition applications. The theoretical formulation of our results is the equivalence of the following three properties for finite-state transducers: determinizability in the sense of the generalized algorithm, finite subsequentiability, and the twins property.

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