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  • Language Technology for Icelandic 2019-2023

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  • Project: Language Technology for Icelandic 2019-2023
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  • Semi-supervised Icelandic-Polish Translation System (22.09)

    This Icelandic-Polish translation model (bi-directional) was trained using fairseq (https://github.com/facebookresearch/fairseq) by means of semi-supervised translation by starting with the mBART50 model. The model was then trained using a multi-task curriculum to first learn to denoise sentences. Then the model was trained to translate using aligned parallel texts. Finally the model was provided with monolingual texts in both Icelandic and Polish with which it iteratively creates back-translations. For the PL-IS direction the model achieves a BLEU score of 27.60 on held out true parallel training data and 15.30 on the out-of-domain Flores devset. For the IS-PL direction the model achieves a score of 27.70 on the true data and 13.30 on the Flores devset. -- Þetta íslensk-pólska þýðingarlíkan (tvíátta) var þjálfað með fairseq (https://github.com/facebookresearch/fairseq) með hálf-sjálfvirkum aðferðum frá mBART50 líkaninu. Líkanið var þjálfað á þremur verkefnum, afruglun, samhliða þýðingum og bakþýðingum sem voru myndaðar á þjálfunartíma. Fyrir PL-IS áttina fæst BLEU skor 27.60 á raun gögnum sem voru tekin til hliðar og 15.30 á Flores þróunargögnunum. Fyrir IS-PL áttina fæst skor 27.70 á raun gögnunum og 13.30 á Flores þróunargögnunum.

  • GreynirTranslate - mBART25 NMT (with layer drop) models for Translations between Icelandic and English (1.0)

    These are the models in http://hdl.handle.net/20.500.12537/125 trained with 40% layer drop. They are suitable for inference using every other layer for optimized inference speed with lower translation performance. We refer to the prior submission for usage and the documentation on layerdrop at https://github.com/pytorch/fairseq/blob/fcca32258c8e8bcc9f9890bf4714fa2f96b6b3e1/examples/layerdrop/README.md. Þessi líkön eru þjálfuð með 40% laga missi (e. layer drop) á líkönunum í http://hdl.handle.net/20.500.12537/125. Þau henta vel til þýðinga þar sem er búið að henda öðru hverju lagi í netinu og þannig er hægt að hraða á þýðingum á kostnað gæða. Leiðbeiningar um notkun netanna er að finna með upphaflegu líkönunum og í notkunarleiðbeiningum Fairseq í https://github.com/pytorch/fairseq/blob/fcca32258c8e8bcc9f9890bf4714fa2f96b6b3e1/examples/layerdrop/README.md.

  • GreynirTranslate - mBART25 NMT models for Translations between Icelandic and English (1.0)

    Provided are a general domain IS-EN and EN-IS translation models developed by Miðeind ehf. They are based on a multilingual BART model (https://arxiv.org/pdf/2001.08210.pdf) and finetuned for translation on parallel and backtranslated data. The model is trained using the Fairseq sequence modeling toolkit by PyTorch. Provided here are a model files, sentencepiece subword-tokenizing model and dictionary files for running the model locally. You can run the scripts infer-enis.sh and infer-isen.sh to test the model by translating sentences command-line. For translating documents and evaluating results you will need to binarize the data using fairseq-preprocess and use fairseq-generate for translating. Please refer to the Fairseq documentation for further information on running a pre-trained model: https://fairseq.readthedocs.io/en/latest/ - Pakkinn inniheldur almenn þýðingarlíkön fyrir áttirnar IS-EN og EN-IS þróuð af Miðeind ehf. Þau eru byggð á margmála BART líkani (https://arxiv.org/pdf/2001.08210.pdf) og fínþjálfuð fyrir þýðingar. Líkönin eru þjálfað með Fairseq og PyTorch. Líkönin sjálf og ásamt sentencepiece tilreiðingarlíkani eru gerð aðgengileg. Skripturnar infer-enis.sh og infer-isen.sh gefa dæmi um hvernig er hægt að keyra líkönin á skipanalínu. Til að þýða stór skjöl og meta niðurstöður þarf að nota fairseq-preprocess skipunina ásamt fairseq-generate. Frekari upplýsingar er að finna í Fairseq leiðbeiningunum: https://fairseq.readthedocs.io/en/latest/