The following claims can be made about finite-state methods for spell-checking: 1) Finite-state language models provide support for morphologically complex languages that word lists, affix stripping and similar approaches do not provide; 2) Weighted finite-state models have expressive power equal to other, state-of-the-art string algorithms used by contemporary spell-checkers; and 3) Finite-state models are at least as fast as other string algorithms for lookup and error correction. In this article, we use some contemporary non-finite-state spell-checking methods as a baseline and perform tests in light of the claims, to evaluate state-of-the-art finitestate spell-checking methods. We verify that finite-state spell-checking systems outperform the traditional approaches for English. We also show that the models for morphologically complex languages can be made to perform on par with English systems.

HFST-Helsinki Finite-State Technology (hfst.sf.net) is a framework for compiling and applying linguistic descriptions with finite-state methods. HFST currently connects some of the most important finite-state tools for creating morphologies and spellers into one open-source platform and supports extending and improving the descriptions with weights to accommodate the modeling of statistical information. HFST offers a path from language descriptions to efficient language applications in key environments and operating systems. HFST also provides an opportunity to exchange transducers between different software providers in order to get the best out of each finite-state library.

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This paper presents the machine translation systems submitted by the Abu-MaTran project for the Finnish-English language pair at the WMT 2015 translation task. We tackle the lack of resources and complex morphology of the Finnish language by (i) crawling parallel and monolingual data from the Web and (ii) applying rule-based and unsupervised methods for morphological segmentation. Several statistical machine translation approaches are evaluated and then combined to obtain our final submissions, which are the top performing English-to-Finnish unconstrained (all automatic metrics) and constrained (BLEU), and Finnish-to-English constrained (TER) systems.

Abstract. There are numerous formats for writing spell-checkers for open-source systems and there are many lexical descriptions for natural languages written in these formats. In this paper, we demonstrate a method for converting Hunspell and related spell-checking lexicons into finite-state automata. We also present a simple way to apply unigram corpus training in order to improve the spellchecking suggestion mechanism using weighted finite-state technology. What we propose is a generic and efficient language-independent framework of weighted finite-state automata for spell-checking in typical open-source software, e.g. Mozilla Firefox, OpenOffice and the Gnome desktop.