En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip).
Their main use should be in-domain translation of social surveys.
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on MCSQ test set (BLEU):
en->de: 67.5 (train: genuine in-domain MCSQ data only)
de->en: 75.0 (train: additional in-domain backtranslated MCSQ data)
(Evaluated using multeval: https://github.com/jhclark/multeval)
NER models for NameTag 2, named entity recognition tool, for English, German, Dutch, Spanish and Czech. Model documentation including performance can be found here: https://ufal.mff.cuni.cz/nametag/2/models . These models are for NameTag 2, named entity recognition tool, which can be found here: https://ufal.mff.cuni.cz/nametag/2 .
NER models for NameTag 2, named entity recognition tool, for English, German, Dutch, Spanish and Czech. Model documentation including performance can be found here: https://ufal.mff.cuni.cz/nametag/2/models . These models are for NameTag 2, named entity recognition tool, which can be found here: https://ufal.mff.cuni.cz/nametag/2 .
Wikipedia plain text data obtained from Wikipedia dumps with WikiExtractor in February 2018.
The data come from all Wikipedias for which dumps could be downloaded at [https://dumps.wikimedia.org/]. This amounts to 297 Wikipedias, usually corresponding to individual languages and identified by their ISO codes. Several special Wikipedias are included, most notably "simple" (Simple English Wikipedia) and "incubator" (tiny hatching Wikipedias in various languages).
For a list of all the Wikipedias, see [https://meta.wikimedia.org/wiki/List_of_Wikipedias].
The script which can be used to get new version of the data is included, but note that Wikipedia limits the download speed for downloading a lot of the dumps, so it takes a few days to download all of them (but one or a few can be downloaded fast).
Also, the format of the dumps changes time to time, so the script will probably eventually stop working one day.
The WikiExtractor tool [http://medialab.di.unipi.it/wiki/Wikipedia_Extractor] used to extract text from the Wikipedia dumps is not mine, I only modified it slightly to produce plaintext outputs [https://github.com/ptakopysk/wikiextractor].
The SynSemClass 3.5 synonym verb lexicon investigates semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English and German-English language resources, i.e., relates verb meanings with respect to contextually-based verb synonymy.
The Czech lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), Vallex (http://hdl.handle.net/11234/1-3524), and CzEngVallex (http://hdl.handle.net/11234/1-1512).
The English lexicon entries are linked to EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (https://uvi.colorado.edu/ and http://verbs.colorado.edu/verbnet/index.html), PropBank (http://propbank.github.io/), Ontonotes (http://clear.colorado.edu/compsem/index.php?page=lexicalresources&sub=ontonotes), and English Wordnet (https://wordnet.princeton.edu/).
The German lexicon entries are linked to Woxikon (https://synonyme.woxikon.de), E-VALBU (https://grammis.ids-mannheim.de/verbvalenz), and GUP (http://alanakbik.github.io/multilingual.html; https://github.com/UniversalDependencies/UD_German-GSD).
The SynSemClass synonym verb lexicon version 4.0 investigates, with respect to contextually-based verb synonymy, semantic ‘equivalence’ of Czech, English, and German verb senses and their valency behavior in parallel Czech-English and German-English language resources. SynSemClass 4.0 is a multilingual event-type ontology based on classes of synonymous verb senses, complemented with semantic roles and links to existing semantic lexicons. The version 4.0 is not only enriched by an additional number of classes but in the context of content hierarchy, some classes have been merged. Compared to the older versions of the lexicon, the novelty is the definitions of classes and the definitions of roles.
Czech lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), Vallex (http://hdl.handle.net/11234/1-3524), and CzEngVallex (http://hdl.handle.net/11234/1-1512). The English lexicon entries are linked to EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (https://uvi.colorado.edu/ and http://verbs.colorado.edu/verbnet/index.html), PropBank (http://propbank.github.io/), Ontonotes (http://clear.colorado.edu/compsem/index.php?page=lexicalresources&sub=ontonotes), and English Wordnet (https://wordnet.princeton.edu/). The German lexicon entries are linked to Woxikon (https://synonyme.woxikon.de), E-VALBU (https://grammis.ids-mannheim.de/verbvalenz), and GUP (http://alanakbik.github.io/multilingual.html; https://github.com/UniversalDependencies/UD_German-GSD).
The SynSemClass synonym verb lexicon version 5.0 is a multilingual resource that enriches previous editions of this event-type ontology with a new language, Spanish. The existing languages, English, Czech and German, are further substantially extended by a larger number of classes. SSC 5.0 data also contain lists (in a separate removed_cms.zip file) with originally (pre-)proposed but later rejected class members. All languages are organized into classes and have links to other lexical sources. In addition to the existing links, links to Spanish sources have been added.
The Spanish entries are linked to
ADESSE (http://adesse.uvigo.es/),
Spanish SenSem (http://grial.edu.es/sensem/lexico?idioma=en),
Spanish WordNet (https://adimen.si.ehu.es/cgi-bin/wei/public/wei.consult.perl),
AnCora (https://clic.ub.edu/corpus/en/ancoraverb_es), and
Spanish FrameNet (http://sfn.spanishfn.org/SFNreports.php).
The English entries are linked to
EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2),
CzEngVallex (http://hdl.handle.net/11234/1-1512),
FrameNet (https://framenet.icsi.berkeley.edu/)
VerbNet (https://uvi.colorado.edu/ and http://verbs.colorado.edu/verbnet/index.html),
PropBank (http://propbank.github.io/),
Ontonotes (http://clear.colorado.edu/compsem/index.php?page=lexicalresources&sub=ontonotes), and
English Wordnet (https://wordnet.princeton.edu/).
Czech entries are linked to
PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F),
Vallex (http://hdl.handle.net/11234/1-3524), and
CzEngVallex (http://hdl.handle.net/11234/1-1512).
The German entries are linked to
Woxikon (https://synonyme.woxikon.de),
E-VALBU (https://grammis.ids-mannheim.de/verbvalenz), and
GUP (http://alanakbik.github.io/multilingual.html and https://github.com/UniversalDependencies/UD_German-GSD).
En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on newstest2020 (BLEU):
en->de: 25.9
de->en: 33.4
(Evaluated using multeval: https://github.com/jhclark/multeval)
Pretrained model weights for the UDify model, and extracted BERT weights in pytorch-transformers format. Note that these weights slightly differ from those used in the paper.