Mika Hämäläinen


2022

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Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities
Mika Hämäläinen | Khalid Alnajjar | Niko Partanen | Jack Rueter
Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities

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Emotion Conditioned Creative Dialog Generation
Khalid Alnajjar | Mika Hämäläinen
Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities

We present a DialGPT based model for generating creative dialog responses that are conditioned based on one of the following emotions: anger, disgust, fear, happiness, pain, sadness and surprise. Our model is capable of producing a contextually apt response given an input sentence and a desired emotion label. Our model is capable of expressing the desired emotion with an accuracy of 0.6. The best performing emotions are neutral, fear and disgust. When measuring the strength of the expressed emotion, we find that anger, fear and disgust are expressed in the most strong fashion by the model.

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Using Graph-Based Methods to Augment Online Dictionaries of Endangered Languages
Khalid Alnajjar | Mika Hämäläinen | Niko Tapio Partanen | Jack Rueter
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

Many endangered Uralic languages have multilingual machine readable dictionaries saved in an XML format. However, the dictionaries cover translations very inconsistently between language pairs, for instance, the Livonian dictionary has some translations to Finnish, Latvian and Estonian, and the Komi-Zyrian dictionary has some translations to Finnish, English and Russian. We utilize graph-based approaches to augment such dictionaries by predicting new translations to existing and new languages based on different dictionaries for endangered languages and Wiktionaries. Our study focuses on the lexical resources for Komi-Zyrian (kpv), Erzya (myv) and Livonian (liv). We evaluate our approach by human judges fluent in the three endangered languages in question. Based on the evaluation, the method predicted good or acceptable translations 77% of the time. Furthermore, we train a neural prediction model to predict the quality of the automatically predicted translations with an 81% accuracy. The resulting extensions to the dictionaries are made available on the online dictionary platform used by the speakers of these languages.

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Automatic Generation of Factual News Headlines in Finnish
Maximilian Koppatz | Khalid Alnajjar | Mika Hämäläinen | Thierry Poibeau
Proceedings of the 15th International Conference on Natural Language Generation

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When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity
Khalid Alnajjar | Mika Hämäläinen | Jörg Tiedemann | Jorma Laaksonen | Mikko Kurimo
Proceedings of the 29th International Conference on Computational Linguistics

Prerecorded laughter accompanying dialog in comedy TV shows encourages the audience to laugh by clearly marking humorous moments in the show. We present an approach for automatically detecting humor in the Friends TV show using multimodal data. Our model is capable of recognizing whether an utterance is humorous or not and assess the intensity of it. We use the prerecorded laughter in the show as annotation as it marks humor and the length of the audience’s laughter tells us how funny a given joke is. We evaluate the model on episodes the model has not been exposed to during the training phase. Our results show that the model is capable of correctly detecting whether an utterance is humorous 78% of the time and how long the audience’s laughter reaction should last with a mean absolute error of 600 milliseconds.

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Help from the Neighbors: Estonian Dialect Normalization Using a Finnish Dialect Generator
Mika Hämäläinen | Khalid Alnajjar | Tuuli Tuisk
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

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2021

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¡Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline
Khalid Alnajjar | Mika Hämäläinen
Proceedings of the Third Workshop on Multimodal Artificial Intelligence

We construct the first ever multimodal sarcasm dataset for Spanish. The audiovisual dataset consists of sarcasm annotated text that is aligned with video and audio. The dataset represents two varieties of Spanish, a Latin American variety and a Peninsular Spanish variety, which ensures a wider dialectal coverage for this global language. We present several models for sarcasm detection that will serve as baselines in the future research. Our results show that results with text only (89%) are worse than when combining text with audio (91.9%). Finally, the best results are obtained when combining all the modalities: text, audio and video (93.1%). Our dataset will be published on Zenodo with access granted by request.

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Overview of Open-Source Morphology Development for the Komi-Zyrian Language: Past and future
Jack Rueter | Niko Partanen | Mika Hämäläinen | Trond Trosterud
Proceedings of the Seventh International Workshop on Computational Linguistics of Uralic Languages

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The Current State of Finnish NLP
Mika Hämäläinen | Khalid Alnajjar
Proceedings of the Seventh International Workshop on Computational Linguistics of Uralic Languages

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Proceedings of the Workshop on Natural Language Processing for Digital Humanities
Mika Hämäläinen | Khalid Alnajjar | Niko Partanen | Jack Rueter
Proceedings of the Workshop on Natural Language Processing for Digital Humanities

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Processing M.A. Castrén’s Materials: Multilingual Historical Typed and Handwritten Manuscripts
Niko Partanen | Jack Rueter | Khalid Alnajjar | Mika Hämäläinen
Proceedings of the Workshop on Natural Language Processing for Digital Humanities

The study forms a technical report of various tasks that have been performed on the materials collected and published by Finnish ethnographer and linguist, Matthias Alexander Castrén (1813–1852). The Finno-Ugrian Society is publishing Castrén’s manuscripts as new critical and digital editions, and at the same time different research groups have also paid attention to these materials. We discuss the workflows and technical infrastructure used, and consider how datasets that benefit different computational tasks could be created to further improve the usability of these materials, and also to aid the further processing of similar archived collections. We specifically focus on the parts of the collections that are processed in a way that improves their usability in more technical applications, complementing the earlier work on the cultural and linguistic aspects of these materials. Most of these datasets are openly available in Zenodo. The study points to specific areas where further research is needed, and provides benchmarks for text recognition tasks.

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TFW2V: An Enhanced Document Similarity Method for the Morphologically Rich Finnish Language
Quan Duong | Mika Hämäläinen | Khalid Alnajjar
Proceedings of the Workshop on Natural Language Processing for Digital Humanities

Measuring the semantic similarity of different texts has many important applications in Digital Humanities research such as information retrieval, document clustering and text summarization. The performance of different methods depends on the length of the text, the domain and the language. This study focuses on experimenting with some of the current approaches to Finnish, which is a morphologically rich language. At the same time, we propose a simple method, TFW2V, which shows high efficiency in handling both long text documents and limited amounts of data. Furthermore, we design an objective evaluation method which can be used as a framework for benchmarking text similarity approaches.

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Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language
Linda Wiechetek | Flammie Pirinen | Mika Hämäläinen | Chiara Argese
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

We investigate both rule-based and machine learning methods for the task of compound error correction and evaluate their efficiency for North Sámi, a low resource language. The lack of error-free data needed for a neural approach is a challenge to the development of these tools, which is not shared by bigger languages. In order to compensate for that, we used a rule-based grammar checker to remove erroneous sentences and insert compound errors by splitting correct compounds. We describe how we set up the error detection rules, and how we train a bi-RNN based neural network. The precision of the rule-based model tested on a corpus with real errors (81.0%) is slightly better than the neural model (79.4%). The rule-based model is also more flexible with regard to fixing specific errors requested by the user community. However, the neural model has a better recall (98%). The results suggest that an approach that combines the advantages of both models would be desirable in the future. Our tools and data sets are open-source and freely available on GitHub and Zenodo.

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Detecting Depression in Thai Blog Posts: a Dataset and a Baseline
Mika Hämäläinen | Pattama Patpong | Khalid Alnajjar | Niko Partanen | Jack Rueter
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs. We experiment with two different LSTM based models and two different BERT based models. We achieve a 77.53% accuracy with a Thai BERT model in detecting depression. This establishes a good baseline for future researcher on the same corpus. Furthermore, we identify a need for Thai embeddings that have been trained on a more varied corpus than Wikipedia. Our corpus, code and trained models have been released openly on Zenodo.

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Apurinã Universal Dependencies Treebank
Jack Rueter | Marília Fernanda Pereira de Freitas | Sidney Da Silva Facundes | Mika Hämäläinen | Niko Partanen
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

This paper presents and discusses the first Universal Dependencies treebank for the Apurinã language. The treebank contains 76 fully annotated sentences, applies 14 parts-of-speech, as well as seven augmented or new features — some of which are unique to Apurinã. The construction of the treebank has also served as an opportunity to develop finite-state description of the language and facilitate the transfer of open-source infrastructure possibilities to an endangered language of the Amazon. The source materials used in the initial treebank represent fieldwork practices where not all tokens of all sentences are equally annotated. For this reason, establishing regular annotation practices for the entire Apurinã treebank is an ongoing project.

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Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered
Mika Hämäläinen | Niko Partanen | Jack Rueter | Khalid Alnajjar
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.

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An Unsupervised method for OCR Post-Correction and Spelling Normalisation for Finnish
Quan Duong | Mika Hämäläinen | Simon Hengchen
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

Historical corpora are known to contain errors introduced by OCR (optical character recognition) methods used in the digitization process, often said to be degrading the performance of NLP systems. Correcting these errors manually is a time-consuming process and a great part of the automatic approaches have been relying on rules or supervised machine learning. We build on previous work on fully automatic unsupervised extraction of parallel data to train a character-based sequence-to-sequence NMT (neural machine translation) model to conduct OCR error correction designed for English, and adapt it to Finnish by proposing solutions that take the rich morphology of the language into account. Our new method shows increased performance while remaining fully unsupervised, with the added benefit of spelling normalisation. The source code and models are available on GitHub and Zenodo.

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The Great Misalignment Problem in Human Evaluation of NLP Methods
Mika Hämäläinen | Khalid Alnajjar
Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)

We outline the Great Misalignment Problem in natural language processing research, this means simply that the problem definition is not in line with the method proposed and the human evaluation is not in line with the definition nor the method. We study this misalignment problem by surveying 10 randomly sampled papers published in ACL 2020 that report results with human evaluation. Our results show that only one paper was fully in line in terms of problem definition, method and evaluation. Only two papers presented a human evaluation that was in line with what was modeled in the method. These results highlight that the Great Misalignment Problem is a major one and it affects the validity and reproducibility of results obtained by a human evaluation.

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Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline
Mika Hämäläinen | Khalid Alnajjar | Niko Partanen | Jack Rueter
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

This study presents a new dataset on rumor detection in Finnish language news headlines. We have evaluated two different LSTM based models and two different BERT models, and have found very significant differences in the results. A fine-tuned FinBERT reaches the best overall accuracy of 94.3% and rumor label accuracy of 96.0% of the time. However, a model fine-tuned on Multilingual BERT reaches the best factual label accuracy of 97.2%. Our results suggest that the performance difference is due to a difference in the original training data. Furthermore, we find that a regular LSTM model works better than one trained with a pretrained word2vec model. These findings suggest that more work needs to be done for pretrained models in Finnish language as they have been trained on small and biased corpora.

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Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers
Mika Hämäläinen | Khalid Alnajjar
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We survey human evaluation in papers presenting work on creative natural language generation that have been published in INLG 2020 and ICCC 2020. The most typical human evaluation method is a scaled survey, typically on a 5 point scale, while many other less common methods exist. The most commonly evaluated parameters are meaning, syntactic correctness, novelty, relevance and emotional value, among many others. Our guidelines for future evaluation include clearly defining the goal of the generative system, asking questions as concrete as possible, testing the evaluation setup, using multiple different evaluation setups, reporting the entire evaluation process and potential biases clearly, and finally analyzing the evaluation results in a more profound way than merely reporting the most typical statistics.

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Linguistic change and historical periodization of Old Literary Finnish
Niko Partanen | Khalid Alnajjar | Mika Hämäläinen | Jack Rueter
Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

In this study, we have normalized and lemmatized an Old Literary Finnish corpus using a lemmatization model trained on texts from Agricola. We analyse the error types that occur and appear in different decades, and use word error rate (WER) and different error types as a proxy for measuring linguistic innovation and change. We show that the proposed approach works, and the errors are connected to accumulating changes and innovations, which also results in a continuous decrease in the accuracy of the model. The described error types also guide further work in improving these models, and document the currently observed issues. We also have trained word embeddings for four centuries of lemmatized Old Literary Finnish, which are available on Zenodo.

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Lemmatization of Historical Old Literary Finnish Texts in Modern Orthography
Mika Hämäläinen | Niko Partanen | Khalid Alnajjar
Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale

Texts written in Old Literary Finnish represent the first literary work ever written in Finnish starting from the 16th century. There have been several projects in Finland that have digitized old publications and made them available for research use. However, using modern NLP methods in such data poses great challenges. In this paper we propose an approach for simultaneously normalizing and lemmatizing Old Literary Finnish into modern spelling. Our best model reaches to 96.3% accuracy in texts written by Agricola and 87.7% accuracy in other contemporary out-of-domain text. Our method has been made freely available on Zenodo and Github.

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Finnish Dialect Identification: The Effect of Audio and Text
Mika Hämäläinen | Khalid Alnajjar | Niko Partanen | Jack Rueter
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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.

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Developing Keyboards for the Endangered Livonian Language
Mika Hämäläinen | Khalid Alnajjar
Proceedings of the Fifth Workshop on Widening Natural Language Processing

We present our current work on developing keyboard layouts for a critically endangered Uralic language called Livonian. Our layouts work on Windows, MacOS and Linux. In addition, we have developed keyboard apps with predictive text for Android and iOS. This work has been conducted in collaboration with the language community.

2020

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Open-Source Morphology for Endangered Mordvinic Languages
Jack Rueter | Mika Hämäläinen | Niko Partanen
Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)

This document describes shared development of finite-state description of two closely related but endangered minority languages, Erzya and Moksha. It touches upon morpholexical unity and diversity of the two languages and how this provides a motivation for shared open-source FST development. We describe how we have designed the transducers so that they can benefit from existing open-source infrastructures and are as reusable as possible.

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Ve’rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource NLP and Community Involvement
Khalid Alnajjar | Mika Hämäläinen | Jack Rueter | Niko Partanen
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

We present an open-source online dictionary editing system, Ve′rdd, that offers a chance to re-evaluate 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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Speech Recognition for Endangered and Extinct Samoyedic languages
Niko Partanen | Mika Hämäläinen | Tiina Klooster
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

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On Editing Dictionaries for Uralic Languages in an Online Environment
Khalid Alnajjar | Mika Hämäläinen | Jack Rueter
Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages

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Morphological Disambiguation of South Sámi with FSTs and Neural Networks
Mika Hämäläinen | Linda Wiechetek
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

We present a method for conducting morphological disambiguation for South Sámi, which is an endangered language. Our method uses an FST-based morphological analyzer to produce an ambiguous set of morphological readings for each word in a sentence. These readings are disambiguated with a Bi-RNN model trained on the related North Sámi UD Treebank and some synthetically generated South Sámi data. The disambiguation is done on the level of morphological tags ignoring word forms and lemmas; this makes it possible to use North Sámi training data for South Sámi without the need for a bilingual dictionary or aligned word embeddings. Our approach requires only minimal resources for South Sámi, which makes it usable and applicable in the contexts of any other endangered language as well.

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FST Morphology for the Endangered Skolt Sami Language
Jack Rueter | Mika Hämäläinen
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

We present advances in the development of a FST-based morphological analyzer and generator for Skolt Sami. Like other minority Uralic languages, Skolt Sami exhibits a rich morphology, on the one hand, and there is little golden standard material for it, on the other. This makes NLP approaches for its study difficult without a solid morphological analysis. The language is severely endangered and the work presented in this paper forms a part of a greater whole in its revitalization efforts. Furthermore, we intersperse our description with facilitation and description practices not well documented in the infrastructure. Currently, the analyzer covers over 30,000 Skolt Sami words in 148 inflectional paradigms and over 12 derivational forms.

2019

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Generating Modern Poetry Automatically in Finnish
Mika Hämäläinen | Khalid Alnajjar
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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.

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Dialect Text Normalization to Normative Standard Finnish
Niko Partanen | Mika Hämäläinen | Khalid Alnajjar
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

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 of 23 distinct Finnish dialects. The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.

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Revisiting NMT for Normalization of Early English Letters
Mika Hämäläinen | Tanja Säily | Jack Rueter | Jörg Tiedemann | Eetu Mäkelä
Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

This paper studies the use of NMT (neural machine translation) as a normalization method for an early English letter corpus. The corpus has previously been normalized so that only less frequent deviant forms are left out without normalization. This paper discusses different methods for improving the normalization of these deviant forms by using different approaches. Adding features to the training data is found to be unhelpful, but using a lexicographical resource to filter the top candidates produced by the NMT model together with lemmatization improves results.

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Co-Operation as an Asymmetric Form of Human-Computer Creativity. Case: Peace Machine
Mika Hämäläinen | Timo Honkela
Proceedings of the First Workshop on NLP for Conversational AI

This theoretical paper identifies a need for a definition of asymmetric co-creativity where creativity is expected from the computational agent but not from the human user. Our co-operative creativity framework takes into account that the computational agent has a message to convey in a co-operative fashion, which introduces a trade-off on how creative the computer can be. The requirements of co-operation are identified from an interdisciplinary point of view. We divide co-operative creativity in message creativity, contextual creativity and communicative creativity. Finally these notions are applied in the context of the Peace Machine system concept.

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Finding Sami Cognates with a Character-Based NMT Approach
Mika Hämäläinen | Jack Rueter
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

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Morphosyntactic Disambiguation in an Endangered Language Setting
Jeff Ens | Mika Hämäläinen | Jack Rueter | Philippe Pasquier
Proceedings of the 22nd Nordic Conference on Computational Linguistics

Endangered Uralic languages present a high variety of inflectional forms in their morphology. This results in a high number of homonyms in inflections, which introduces a lot of morphological ambiguity in sentences. Previous research has employed constraint grammars to address this problem, however CGs are often unable to fully disambiguate a sentence, and their development is labour intensive. We present an LSTM based model for automatically ranking morphological readings of sentences based on their quality. This ranking can be used to evaluate the existing CG disambiguators or to directly morphologically disambiguate sentences. Our approach works on a morphological abstraction and it can be trained with a very small dataset.

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Let’s FACE it. Finnish Poetry Generation with Aesthetics and Framing
Mika Hämäläinen | Khalid Alnajjar
Proceedings of the 12th International Conference on Natural Language Generation

We present a creative poem generator for the morphologically rich Finnish language. Our method falls into the master-apprentice paradigm, where a computationally creative genetic algorithm teaches a BRNN model to generate poetry. We model several parts of poetic aesthetics in the fitness function of the genetic algorithm, such as sonic features, semantic coherence, imagery and metaphor. Furthermore, we justify the creativity of our method based on the FACE theory on computational creativity and take additional care in evaluating our system by automatic metrics for concepts together with human evaluation for aesthetics, framing and expressions.

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From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction
Mika Hämäläinen | Simon Hengchen
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

A great deal of historical corpora suffer from errors introduced by the OCR (optical character recognition) methods used in the digitization process. Correcting these errors manually is a time-consuming process and a great part of the automatic approaches have been relying on rules or supervised machine learning. We present a fully automatic unsupervised way of extracting parallel data for training a character-based sequence-to-sequence NMT (neural machine translation) model to conduct OCR error correction.

2018

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Development of an Open Source Natural Language Generation Tool for Finnish
Mika Hämäläinen | Jack Rueter
Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages

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Normalizing Early English Letters to Present-day English Spelling
Mika Hämäläinen | Tanja Säily | Jack Rueter | Jörg Tiedemann | Eetu Mäkelä
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

This paper presents multiple methods for normalizing the most deviant and infrequent historical spellings in a corpus consisting of personal correspondence from the 15th to the 19th century. The methods include machine translation (neural and statistical), edit distance and rule-based FST. Different normalization methods are compared and evaluated. All of the methods have their own strengths in word normalization. This calls for finding ways of combining the results from these methods to leverage their individual strengths.

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Poem Machine - a Co-creative NLG Web Application for Poem Writing
Mika Hämäläinen
Proceedings of the 11th International Conference on Natural Language Generation

We present Poem Machine, an interactive online tool for co-authoring Finnish poetry with a computationally creative agent. Poem Machine can produce poetry of its own and assist the user in authoring poems. The main target group for the system is primary school children, and its use as a part of teaching is currently under study.

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A Master-Apprentice Approach to Automatic Creation of Culturally Satirical Movie Titles
Khalid Alnajjar | Mika Hämäläinen
Proceedings of the 11th International Conference on Natural Language Generation

Satire has played a role in indirectly expressing critique towards an authority or a person from time immemorial. We present an autonomously creative master-apprentice approach consisting of a genetic algorithm and an NMT model to produce humorous and culturally apt satire out of movie titles automatically. Furthermore, we evaluate the approach in terms of its creativity and its output. We provide a solid definition for creativity to maximize the objectiveness of the evaluation.

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Combining Concepts and Their Translations from Structured Dictionaries of Uralic Minority Languages
Mika Hämäläinen | Liisa Lotta Tarvainen | Jack Rueter
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Synchronized Mediawiki based analyzer dictionary development
Jack Rueter | Mika Hämäläinen
Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages