We compare different LSTMs and transformer models in terms of their effectiveness in normalizing dialectal Finnish into the normative standard Finnish. As dialect is the common way of communication for people online in Finnish, such a normalization is a necessary step to improve the accuracy of the existing Finnish NLP tools that are tailored for normative Finnish text. We work on a corpus consisting of dialectal data from 23 distinct Finnish dialect varieties. The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.

We present an open source Python library to automatically produce syntactically correct Finnish sentences when only lemmas and their relations are provided. The tool resolves automatically morphosyntax in the sentence such as agreement and government rules and uses Omorfi to produce the correct morphological forms. In this paper, we discuss how case government can be learned automatically from a corpus and incorporated as a part of the natural language generation tool. We also present how agreement rules are modelled in the system and discuss the use cases of the tool such as its initial use as part of a computational creativity system, called Poem Machine. Tiivistelmä Tässä artikkelissa esittelemme avoimen lähdekoodin Python-kirjaston kieliopillisten lauseiden automaattista tuottamista varten suomen kielelle. Kieliopilliset rakenteet pystytään tuottamaan pelkkien lemmojen ja niiden välisten suhteiden avulla. Työkalu ratkoo vaadittavan morfosyntaktiset vaatimukset kuten kongruenssin ja rektion automaattisesti ja tuottaa morfologisesti oikean muodon Omorfin avulla. Esittelemme tavan, jolla verbien rektiot voidaan poimia automaattisesti korpuksesta ja yhdistää osaksi NLG-järjestelmää. Esittelemme, miten kongruenssi on mallinnettu osana järjestelmää ja kuvaamme työkalun alkuperäisen käyttötarkoituksen osana laskennallisesti luovaa Runokone-järjestelmää.

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Often, there is disagreement about who is the better athlete, or the better team. The aim of this paper is to clarify a recent disagreement between the author (Mika Hämäläinen) and Arvi Pakaslahti about different views of 'betterness' in sport competitions. I introduced a 'three criteria' model of betterness, which suggested the following three criteria: the official result, the ideally adjudicated result and the display of athletic skills. Pakaslahti criticised my account and introduced his own model, which has two built-in ideals of sport competitions: the Athletic Superiority Ideal and the Just Results Ideal. I argue that when we look behind the terminological differences, there is surprisingly little genuine disagreement between my account and Pakaslahti's.

We present an open-source online dictionary editing system, Ve rdd, that offers a chance to reevaluate and edit grassroots dictionaries that have been exposed to multiple amateur editors. The idea is to incorporate community activities into a state-of-the-art finite-state language description of a seriously endangered minority language, Skolt Sami. Problems involve getting the community to take part in things above the pencil-and-paper level. At times, it seems that the native speakers and the dictionary oriented are lacking technical understanding to utilize the infrastructures which might make their work more meaningful in the future, i.e. multiple reuse of all of their input. Therefore, our system integrates with the existing tools and infrastructures for Uralic language masking the technical complexities behind a user-friendly UI.

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We present a novel approach for generating poetry automatically for the morphologically rich Finnish language by using a genetic algorithm. The approach improves the state of the art of the previous Finnish poem generators by introducing a higher degree of freedom in terms of structural creativity. Our approach is evaluated and described within the paradigm of computational creativity, where the fitness functions of the genetic algorithm are assimilated with the notion of aesthetics. The output is considered to be a poem 81.5% of the time by human evaluators.

Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo.