Descriptional Complexity of Iterated Uniform Finite-State Transducers

Input-driven pushdown automata (IDPDA) are pushdown automata where the next action on the pushdown store (push, pop, nothing) is solely governed by the input symbol. Nowadays such devices are usually defined such that popping from the empty pushdown does not block the computation but continues it with empty pushdown. Here, we consider IDPDAs that have a more balanced behavior concerning pushing and popping. Digging input-driven pushdown automata (DIDPDA) are basically IDPDAs that, when forced to pop from the empty pushdown, dig a hole of the shape of the popped symbol in the bottom of the pushdown. Popping further symbols from a pushdown having a hole at the bottom deepens the current hole furthermore. The hole can only be filled up by pushing symbols previously popped. We study the impact of the new behavior of DIDPDAs on their power and compare their capacities with the capacities of ordinary IDPDAs and tinput-driven pushdown automata which are basically IDPDAs whose input may be preprocessed by length-preserving finite state transducers. It turns out that the capabilities are incomparable. We address the determinization of DIDPDAs and their descriptional complexity, closure properties, and decidability questions.

Download Full-text

Iterated Uniform Finite-State Transducers: Descriptional Complexity of Nondeterminism and Two-Way Motion

Descriptional Complexity of Formal Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-62536-8_10 ◽

2020 ◽

pp. 117-129

Author(s):

Martin Kutrib ◽

Andreas Malcher ◽

Carlo Mereghetti ◽

Beatrice Palano

Keyword(s):

Descriptional Complexity ◽

Finite State Transducers ◽

Finite State

Download Full-text

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.

Download Full-text

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.

Download Full-text