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  • 151.
    Östling, Robert
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Tiedemann, Jörg
    Continuous multilinguality with language vectors2017In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Short Papers / [ed] Mirella Lapata, Phil Blunsom, Alexander Koller, Association for Computational Linguistics, 2017, Vol. 2, p. 644-649Conference paper (Refereed)
    Abstract [en]

    Most existing models for multilingual natural language processing (NLP) treat language as a discrete category, and make predictions for either one language or the other. In contrast, we propose using continuous vector representations of language. We show that these can be learned efficiently with a character-based neural language model, and used to improve inference about language varieties not seen during training. In experiments with 1303 Bible translations into 990 different languages, we empirically explore the capacity of multilingual language models, and also show that the language vectors capture genetic relationships between languages.

  • 152.
    Östling, Robert
    et al.
    University of Helsinki, Finland.
    Tiedemann, Jörg
    University of Helsinki, Finland.
    Efficient word alignment with Markov Chain Monte Carlo2016In: Prague Bulletin of Mathematical Linguistics, ISSN 0032-6585, E-ISSN 1804-0462, no 106, p. 125-146Article in journal (Refereed)
    Abstract [en]

    We present efmaral, a new system for efficient and accurate word alignment using a Bayesian model with Markov Chain Monte Carlo (MCMC) inference. Through careful selection of data structures and model architecture we are able to surpass the fast_align system, commonly used for performance-critical word alignment, both in computational efficiency and alignment accuracy. Our evaluation shows that a phrase-based statistical machine translation (SMT) system produces translations of higher quality when using word alignments from efmaral than from fast_align, and that translation quality is on par with what is obtained using giza++, a tool requiring orders of magnitude more processing time. More generally we hope to convince the reader that Monte Carlo sampling, rather than being viewed as a slow method of last resort, should actually be the method of choice for the SMT practitioner and others interested in word alignment.

  • 153.
    Östling, Robert
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Compounding in a Swedish Blog Corpus2013In: Computer mediated discourse across languages / [ed] Laura Álvarez López, Charlotta Seiler Brylla & Philip Shaw, Stockholm: Acta Universitatis Stockholmiensis, 2013, p. 45-63Chapter in book (Refereed)
1234 151 - 153 of 153
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