A bimachine compiler for ranked tagging rules

Skut, Wojciech; Ulrich, Stefan; Hammervold, Kathrine

doi:10.3115/1220355.1220384

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…A typical strategy chooses always the most reliable rule and the leftmost target position. When the plain contextual tests are combined with the application strategy, we obtain a total functional transducer (Skut et al, 2004; ? ; ?).…”

Section: Scg As a "Phonological" Grammarmentioning

confidence: 99%

The Power of Constraint Grammars Revisited

Yli-Jyrä

2017

Preprint

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Sequential Constraint Grammar (SCG) (Karlsson, 1990) and its extensions have lacked clear connections to formal language theory. The purpose of this article is to lay a foundation for these connections by simplifying the definition of strings processed by the grammar and by showing that Nonmonotonic SCG is undecidable and that derivations similar to the Generative Phonology exist. The current investigations propose resource bounds that restrict the generative power of SCG to a subset of context sensitive languages and present a strong finite-state condition for grammars as wholes. We show that a grammar is equivalent to a finite-state transducer if it is implemented with a Turing machine that runs in o(n log n) time. This condition opens new finite-state hypotheses and avenues for deeper analysis of SCG instances in the way inspired by Finite-State Phonology.

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Section: Scg As a "Phonological" Grammarmentioning

confidence: 99%

The Power of Constraint Grammars Revisited

Yli-Jyrä

2017

Preprint

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“…Following on the Bell Labs work, FST-based approaches to text normalization were pursued at Rhetorical Systems (now part of Nuance) (Skut, Ulrich and Hammervold 2003, 2004), as well as in research projects at the University of Stuttgart (Möbius 2001).…”

Section: History Of Fst-based Approaches To Text Normalizationmentioning

confidence: 99%

The Kestrel TTS text normalization system

Ebden

Sproat

2014

Nat. Lang. Eng.

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This paper describes the Kestrel text normalization system, a component of the Google textto-speech synthesis (TTS) system. At the core of Kestrel are text-normalization grammars that are compiled into libraries of weighted finite-state transducers (WFSTs). While the use of WFSTs for text normalization is itself not new, Kestrel differs from previous systems in its separation of the initial tokenization and classification phase of analysis from verbalization. Input text is first tokenized and different tokens classified using WFSTs. As part of the classification, detected semiotic classes -expressions such as currency amounts, dates, times, measure phases, are parsed into protocol buffers (https://code.google.com/p/protobuf/). The protocol buffers are then verbalized, with possible reordering of the elements, again using WFSTs. This paper describes the architecture of Kestrel, the protocol buffer representations of semiotic classes, and presents some examples of grammars for various languages. We also discuss applications and deployments of Kestrel as part of the Google TTS system, which runs on both server and client side on multiple devices, and is used daily by millions of people in nineteen languages and counting.

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A bimachine compiler for ranked tagging rules

Cited by 2 publications

References 11 publications

The Power of Constraint Grammars Revisited

The Power of Constraint Grammars Revisited

The Kestrel TTS text normalization system

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