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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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  • Long Context Translation Models for English-Icelandic translations (22.09)

    ENGLISH: These models are capable of translating between English and Icelandic, in both directions. They are capable of translating several sentences at once and are robust to some input errors such as spelling errors. The models are based on the pretrained mBART25 model (http://hdl.handle.net/20.500.12537/125, https://arxiv.org/abs/2001.08210) and finetuned on bilingual EN-IS data and backtranslated data (including http://hdl.handle.net/20.500.12537/260). The full backtranslation data used includes texts from the following sources: The Icelandic Gigaword Corpus (Without sport) (IGC), The Icelandic Common Crawl Corpus (IC3), Student theses (skemman.is), Greynir News, Wikipedia, Icelandic sagas, Icelandic e-books, Books3, NewsCrawl, Wikipedia, EuroPARL, Reykjavik Grapevine, Iceland Review. The true parallel long context data used is from European Economic Area (EEA) regulations, document-level Icelandic Student Theses Abstracts corpus (IPAC), Stúdentablaðið (university student magazine), The report of the Special Investigation Commision (Rannsóknarnefnd Alþingis), The Bible and Jehovah’s witnesses corpus (JW300). Provided here are model files, a SentencePiece subword-tokenizing model and dictionary files for running the model locally along with scripts for translating sentences on the command line. We refer to the included README for instructions on running inference. ÍSLENSKA: Þessi líkön geta þýtt á milli ensku og íslensku. Líkönin geta þýtt margar málsgreinar í einu og eru þolin gagnvart villum og smávægilegu fráviki í inntaki. Líkönin eru áframþjálfuð þýðingarlíkön sem voru þjálfuð frá mBART25 líkaninu (http://hdl.handle.net/20.500.12537/125, https://arxiv.org/abs/2001.08210). Þjálfunargögin eru samhlíða ensk-íslensk gögn ásamt bakþýðingum (m.a. http://hdl.handle.net/20.500.12537/260). Einmála gögn sem voru bakþýdd og nýtt í þjálfanir eru fengin úr: Risamálheildinni (án íþróttafrétta), Icelandic Common Crawl Corpus (IC3), ritgerðum af skemman.is, fréttum í fréttagrunni Greynis, Wikipedia, íslendingasögurnar, opnar íslenskar rafbækur, Books3, NewsCrawl, Wikipedia, EuroPARL, Reykjavik Grapevine, Iceland Review. Samhliða raungögn eru fengin upp úr European Economic Area (EEA) reglugerðum, samröðuðum útdráttum úr ritgerðum nemenda (IPAC), Stúdentablaðið, Skýrsla Rannsóknarnefndar Alþingis, Biblíunni og samhliða málheild unna úr Varðturninum (JW300). Útgefin eru líkönin sjálf, orðflísunarlíkan og orðabók fyrir flísunina, ásamt skriptum til að keyra þýðingar frá skipanalínu. Nánari leiðbeiningar eru í README skjalinu.

  • 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.

  • Optimized Long Context Translation Models for English-Icelandic translations (22.09)

    ENGLISH: These models are optimized versions of the translation models released in http://hdl.handle.net/20.500.12537/278. Instead of the 24 layers used in the full model, they have been shrunk down to 7 layers. The computational resources required to run inference on the models is thus significantly less than using the original models. Performance is comparable to the original models when evaluated on general topics such as news, but for expert knowledge from the training data (e.g. EEA regulations) the original models are more capable. The models are capable of translating between English and Icelandic, in both directions. They are capable of translating several sentences at once and are robust to some input errors such as spelling errors. The models are based on the pretrained mBART25 model (http://hdl.handle.net/20.500.12537/125, https://arxiv.org/abs/2001.08210) and finetuned on bilingual EN-IS data and backtranslated data (including http://hdl.handle.net/20.500.12537/260). The full backtranslation data used includes texts from the following sources: The Icelandic Gigaword Corpus (Without sport) (IGC), The Icelandic Common Crawl Corpus (IC3), Student theses (skemman.is), Greynir News, Wikipedia, Icelandic sagas, Icelandic e-books, Books3, NewsCrawl, Wikipedia, EuroPARL, Reykjavik Grapevine, Iceland Review. The true parallel long context data used is from European Economic Area (EEA) regulations, document-level Icelandic Student Theses Abstracts corpus (IPAC), Stúdentablaðið (university student magazine), The report of the Special Investigation Commision (Rannsóknarnefnd Alþingis), The Bible and Jehovah’s witnesses corpus (JW300). Provided here are model files, a SentencePiece subword-tokenizing model and dictionary files for running the model locally along with scripts for translating sentences on the command line. We refer to the included README for instructions on running inference. ÍSLENSKA: Þessi líkön eru smækkaðar útgáfur af líkönunum sem má finna á http://hdl.handle.net/20.500.12537/278 . Upphaflegu líkönin eru með 24 lög en þessar útgáfur eru með 7 lög og eru skilvirkari í keyrslu. Frammistaða líkananna er á pari við þau upphaflegu fyrir almennan texta, svo sem í fréttum. Á sérhæfðari texta sem er að finna í þjálfunargögnunum standa þau sig verr, t.d. á evrópureglugerðum. Þessi líkön geta þýtt á milli ensku og íslensku. Líkönin geta þýtt margar málsgreinar í einu og eru þolin gagnvart villum og smávægilegu fráviki í inntaki. Líkönin eru áframþjálfuð þýðingarlíkön sem voru þjálfuð frá mBART25 líkaninu (http://hdl.handle.net/20.500.12537/125, https://arxiv.org/abs/2001.08210). Þjálfunargögin eru samhliða ensk-íslensk gögn ásamt bakþýðingum (m.a. http://hdl.handle.net/20.500.12537/260). Einmála gögn sem voru bakþýdd og nýtt í þjálfanir eru fengin úr: Risamálheildinni (án íþróttafrétta), Icelandic Common Crawl Corpus (IC3), ritgerðum af skemman.is, fréttum í fréttagrunni Greynis, Wikipedia, Íslendingasögunum, opnum íslenskum rafbókum, Books3, NewsCrawl, Wikipedia, EuroPARL, Reykjavik Grapevine, Iceland Review. Samhliða raungögn eru fengin upp úr European Economic Area (EEA) reglugerðum, samröðuðum útdráttum úr ritgerðum nemenda (IPAC), Stúdentablaðinu, Skýrslu Rannsóknarnefndar Alþingis, Biblíunni og samhliða málheild unna úr Varðturninum (JW300). Útgefin eru líkönin sjálf, orðflísunarlíkan og orðabók fyrir flísunina, ásamt skriptum til að keyra þýðingar frá skipanalínu. Nánari leiðbeiningar eru í README skjalinu.

  • 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/

  • GreynirT2T - En--Is NMT with Tensor2Tensor (1.0)

    A program library for training English-Icelandic neural machine translation systems, built on top of Tensor2Tensor and Tensorflow. Supports training with or without back-translated data. Forritasafn til að þjálfa þýðingarlíkön sem þýða milli íslensku og ensku. Uppsetningin er byggð á Tensor2Tensor og Tensorflow. Safnið styður þjálfun með og án bakþýðingargagna.

  • GreynirSeq Domain Translation Pipeline (22.06)

    This is a pipeline for creating GreynirSeq domain-aware translation models. A valid checkpoint of a base translation model based on mBART25 can be finetuned as a domain translation model. The resulting model can be queried using a label for the requested domain. We recommend the English -- Icelandic translation models available in https://repository.clarin.is/repository/xmlui/handle/20.500.12537/125 . The included preprocess script expects a .tsv input file with the three fields (domains, english, icelandic), this is the training corpus. The script finetune.sh can be run to fine tune the model until convergence. Finally, one can run evaluate.sh to compute BLEU over the development set of Flores. See the README file for further details on setting up an environment and fetching data.

  • GreynirT2T Serving - En--Is NMT Inference and Pre-trained Models (1.0)

    Code and models required to run the GreynirT2T Transformer NMT system for translation between English and Icelandic. Includes a Docker-Compose file that starts a REST web server making the translation models available to clients. Forrit og líkön til að keyra GreynirT2T Transformer vélþýðingarlíkön fyrir þýðingar á milli íslensku og ensku. Docker-Compose uppskrift keyrir upp REST vefþjón sem gerir líkönin aðgengileg netbiðlurum.