Path-equivalent developments in acyclic weighted automata

International audienceWeighted finite automata (WFA) are used with FPGA accelerating hardware to scan large genomic banks. Hardwiring such automata raises surface area and clock frequency constraints, requiring efficient ε-transitions-removal techniques. In this paper, we present bounds on the number of new transitions for the development of acyclic WFA, which is a special case of the ε-transitions-removal problem. We introduce a new problem, a partial removal of ε-transitions while accepting short chains of ε-transitions

Statistical language models within the algebra of weighted rational languages

Statistical language models are an important tool in natural language processing. They represent prior knowledge about a certain language which is usually gained from a set of samples called a corpus. In this paper, we present a novel way of creating N-gram language models using weighted finite automata. The construction of these models is formalised within the algebra underlying weighted finite automata and expressed in terms of weighted rational languages and transductions. Besides the algebra we make use of five special constant weighted transductions which rely only on the alphabet and the model parameter N. In addition, we discuss efficient implementations of these transductions in terms of virtual constructions

Pushdown automata in statistical machine translation

This article describes the use of pushdown automata (PDA) in the context of statistical machine translation and alignment under a synchronous context-free grammar. We use PDAs to compactly represent the space of candidate translations generated by the grammar when applied to an input sentence. General-purpose PDA algorithms for replacement, composition, shortest path, and expansion are presented. We describe HiPDT, a hierarchical phrase-based decoder using the PDA representation and these algorithms. We contrast the complexity of this decoder with a decoder based on a finite state automata representation, showing that PDAs provide a more suitable framework to achieve exact decoding for larger synchronous context-free grammars and smaller language models. We assess this experimentally on a large-scale Chinese-to-English alignment and translation task. In translation, we propose a two-pass decoding strategy involving a weaker language model in the first-pass to address the results of PDA complexity analysis. We study in depth the experimental conditions and tradeoffs in which HiPDT can achieve state-of-the-art performance for large-scale SMT. </jats:p

Analysing the Efficiency of Algorithms for Compiling Finite-State Morphologies

Äärellistilaiset morfologiat ovat tietokoneohjelmia, jotka mallintavat kielen sanojen rakennetta (morfologiaa) merkkijonopareja sisältävillä tietorakenteilla (äärellistilaisilla transduktoreilla). Äärellistilaisia morfologioita voidaan käyttää esimerkiksi hakuohjelmissa, jotka löytävät tekstistä kaikki annetun perusmuotoisen sanan esiintymät eri taivutusmuodoissaan. Äärellistilaiset morfologiat ovat myös hyödyllisiä, kun tekstistä tehdään tilastoja siitä kuinka usein kukin sana esiintyy ja missä taivutusmuodoissa. Äärellistilaisten morfologioiden rakentaminen on monimutkainen prosessi, johon kuuluu useita tehtäviä, joista yksi on transduktorin minimointi. Yleisiä minimointialgoritmeja ovat Brzozowskin (BRZ) ja Hopcroftin algoritmit (HOP). Kirjallisuudessa esiintyy väitteitä, joiden mukaan BRZ:n ja HOP:n välinen ero on merkityksettömän pieni morfologioita käännettäessä. Kuitenkaan BRZ:n suorituskykyä ei ole järjestelmällisesti testattu tai verrattu HOP:iin missään tutkimuksessa. Tässä diplomityössä käännettiin HFST-ohjelmistolla kaksi avoimen lähdekoodin morfologiaa, suomelle kirjoitettu OMorFi ja saksalle kirjoitettu Morphisto. HFST perustuu kahteen avoimen lähdekoodin transduktoriohjelmistopakettiin, SFST:hen ja OpenFst:hen, joista edellinen käyttää BRZ:ia ja jälkimmäinen HOP:ia minimointialgoritmina. BRZ osoittautui paljon hitaammaksi kuin HOP sekä suomen että saksan morfologioilla. BRZ:n hitaus oli ilmeistä transduktoreissa, jotka sisälsivät suuren mittakaavan syklisyyttä eli niissä oli siirtymiä, jotka johtivat lopputilojen läheisyydestä alkutilan läheisyyteen. Tällaisia transduktoreita esiintyy usein morfologioissa, joissa on yhdyssanamekanismi. Jos HOP:n ja BRZ:n välillä on valittava, edellinen on parempi vaihtoehto minimointi-algoritmiksi. BRZ on joskus nopeampi kuin HOP, mutta siinä tapauksessa algoritmien ero on melko pieni. Niissä tapauksissa joissa BRZ on hitaampi kuin HOP, ero on huomattavasti suurempi: BRZ on joskus jopa 50 kertaa hitaampi kuin HOP. BRZ on kuitenkin paljon helpompi toteuttaa, koska se perustuu kahteen perusoperaatioon, determinisointiin ja reversioon. Jos HOP:n toteuttaminen on liian vaativa tehtävä, avoimen lähdekoodin transduktorikirjaston kehittäjät voivat käyttää OpenFst:n minimointialgoritmia. Transduktorit voidaan muuntaa OpenFst:n muotoon, minimoida OpenFst:llä ja muuntaa takaisin alkuperäiseen muotoon. Tätä ratkaisua on tarkoitus käyttää myös HFST:n tulevissa versioissa.Finite-state morphologies (FSMs) are computer programs that model the structure of words in a language (morphology) with networks containing a number of string pairs (finite-state transducers). FSMs can be used e.g. to implement search programs that can find all forms of a word in a document if they are given only the base form. FSMs are also useful in compiling statistics on a text, i.e. finding out how often a word occurs and in which forms. Constructing FSMs is a complex process involving many tasks, one of which is transducer minimisation. Common minimisation algorithms include Brzozowski's (BRZ) and Hopcroft's algorithm (HOP). There have been claims in the literature that often the difference between BRZ and HOP is insignificant when compiling FSMs. However, no studies have been carried out where the performance of BRZ would have been systematically tested or compared with HOP. In this thesis, we compiled two open-source morphologies, OMorFi for Finnish and Morphisto for German, with the HFST software. HFST is based on two open-source transducer software packages, SFST and OpenFst, the former using BRZ and the latter HOP as a minimisation algorithm. BRZ turned out to be much slower than HOP both on Finnish and German morphologies. The slowness of BRZ was evident in transducers that contained large-scale cyclicity, i.e. had transitions leading from the nearness of the final states to the nearness of initial states. These kinds of transducers often occur in morphologies that have a compounding mechanism. If a choice must be made between HOP and BRZ, the previous is a better choice for a minimisation algorithm. BRZ is sometimes faster than HOP, but in that case their difference is quite small. In the cases where BRZ is slower than HOP, their difference is much bigger, BRZ sometimes being 50 times slower than HOP. Of course, BRZ is much easier to implement since it uses two basic operations, determinisation and reversion. If the implementation of HOP is considered too demanding a task, the developers of free-source transducer libraries can use OpenFst's minimisation algorithm. The transducers can be converted to OpenFst format, minimised with OpenFst and converted back to the original format. This solution will also be used in future versions of HFST

Building and Using Existing Hunspell Dictionaries and TEX Hyphenators as Finite-State Automata

Volume: 5 Proceeding volume: 5There are numerous formats for writing spellcheckers for open-source systems and there are many descriptions for languages written in these formats. Similarly, for word hyphenation by computer there are TEX rules for many languages. In this paper we demonstrate a method for converting these spell-checking lexicons and hyphenation rule sets into finite-state automata, and present a new finite-state based system for writer’s tools used in current open-source software such as Firefox, OpenOffice.org and enchant via the spell-checking library voikko.Peer reviewe

Multi-dimensional Boltzmann Sampling of Languages

This paper addresses the uniform random generation of words from a context-free language (over an alphabet of size $k$), while constraining every letter to a targeted frequency of occurrence. Our approach consists in a multidimensional extension of Boltzmann samplers \cite{Duchon2004}. We show that, under mostly \emph{strong-connectivity} hypotheses, our samplers return a word of size in $[(1-\varepsilon)n, (1+\varepsilon)n]$ and exact frequency in $\mathcal{O}(n^{1+k/2})$ expected time. Moreover, if we accept tolerance intervals of width in $\Omega(\sqrt{n})$ for the number of occurrences of each letters, our samplers perform an approximate-size generation of words in expected $\mathcal{O}(n)$ time. We illustrate these techniques on the generation of Tetris tessellations with uniform statistics in the different types of tetraminoes.Comment: 12p

Weighted finite-state transducers in speech recognition : a compaction algorithm for non-determinizable transducers

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Robust Grammatical Analysis for Spoken Dialogue Systems

We argue that grammatical analysis is a viable alternative to concept spotting for processing spoken input in a practical spoken dialogue system. We discuss the structure of the grammar, and a model for robust parsing which combines linguistic sources of information and statistical sources of information. We discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input.Comment: Accepted for JNL

Statistical and Computational Models for Whole Word Morphology

Das Ziel dieser Arbeit ist die Formulierung eines Ansatzes zum maschinellen Lernen von Sprachmorphologie, in dem letztere als Zeichenkettentransformationen auf ganzen Wörtern, und nicht als Zerlegung von Wörtern in kleinere stukturelle Einheiten, modelliert wird. Der Beitrag besteht aus zwei wesentlichen Teilen: zum einen wird ein Rechenmodell formuliert, in dem morphologische Regeln als Funktionen auf Zeichenketten definiert sind. Solche Funktionen lassen sich leicht zu endlichen Transduktoren übersetzen, was eine solide algorithmische Grundlage für den Ansatz liefert. Zum anderen wird ein statistisches Modell für Graphen von Wortab\-leitungen eingeführt. Die Inferenz in diesem Modell erfolgt mithilfe des Monte Carlo Expectation Maximization-Algorithmus und die Erwartungswerte über Graphen werden durch einen Metropolis-Hastings-Sampler approximiert. Das Modell wird auf einer Reihe von praktischen Aufgaben evaluiert: Clustering flektierter Formen, Lernen von Lemmatisierung, Vorhersage von Wortart für unbekannte Wörter, sowie Generierung neuer Wörter