Mika Hämäläinen

University of Helsinki

Papers bookmarked by Mika Hämäläinen

arXiv.org papers

  • This paper presents work on modelling the social psychological aspect of socialization in the case of a computationally creative master-apprentice system. In each master-apprentice pair, the master, a genetic algorithm, is seen as a parent for its apprentice, which is an NMT based sequence-to-sequence model. The effect of different parenting styles on the creative output of each pair is in the focus of this study. This approach brings a novel view point to computational social creativity, which has mainly focused in the past on computationally creative agents being on a socially equal level, whereas our approach studies the phenomenon in the context of a social hierarchy.
  • 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.
  • 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.
  • We present a method for conducting morphological disambiguation for South S\'ami, 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 S\'ami UD Treebank and some synthetically generated South S\'ami data. The disambiguation is done on the level of morphological tags ignoring word forms and lemmas; this makes it possible to use North S\'ami training data for South S\'ami without the need for a bilingual dictionary or aligned word embeddings. Our approach requires only minimal resources for South S\'ami, which makes it usable and applicable in the contexts of any other endangered language as well.