Marian NMT model for Catalan to Occitan translation. Primary CUNI submission for WMT21 Multilingual
Low-Resource Translation for Indo-European Languages Shared Task.
Marian multilingual translation model from Catalan into Romanian, Italian and Occitan. Primary CUNI submission for WMT21 Multilingual
Low-Resource Translation for Indo-European Languages Shared Task.
Dictionaries with different representations for various languages. Representations include brown clusters of different sizes and morphological dictionaries extracted using different morphological analyzers. All representations cover the most frequent 250,000 word types on the Wikipedia version of the respective language.
Analzers used: MAGYARLANC (Hungarian, Zsibrita et al. (2013)), FREELING (English and Spanish, Padro and Stanilovsky (2012)), SMOR (German, Schmid et al. (2004)), an MA from Charles University (Czech, Hajic (2001)) and LATMOR (Latin, Springmann et al. (2014)).
German has various homophonous sibilant fricatives of phonemic or morphemic nature that can appear in word-final position. In English, the functional status of a word-final \s\ influences its durational properties, with phonemic \s\ being longer than morphemic types. The data set presented here is a small selection of laboratory-elicited German sentences containing various words with final sibilant phonemes (e.g., "das Haus") and morphemes (plural, genitive, clitic, inflection). Durations of the \s\ types were measured and compared across the conditions. An ANOVA between the \s\ types and post-hoc Tukey pair-wise comparisons are presented that show various significant differences.
The submission consists of a csv data file, containing a number of variables, and a PDF document detailing the experiment and variables.
Czech translation of WordSim353. The Czech translation of English WordSim353 word pairs were obtained from four translators. All translation variants were scored according to the lexical similarity/relatedness annotation instructions for WordSim353 annotators, by 25 Czech annotators. The resulting data set consists of two annotation files: "WordSim353-cs.csv" and "WordSim-cs-Multi.csv". Both files are encoded in UTF-8, have a header, text is enclosed in double quotes, and columns are separated by commas. The rows are numbered. The WordSim-cs-Multi data set has rows numbered from 1 to 634, whereas the row indices in the WordSim353-cs data set reflect the corresponding row numbers in the WordSim-cs-Multi data set.
The WordSim353-cs file contains a one-to-one mapping selection of 353 Czech equivalent pairs whose judgments have proven to be most similar to the judgments of their corresponding English originals (compared by the absolute value of the difference between the means over all annotators in each language counterpart). In one case ("psychology-cognition"), two Czech equivalent pairs had identical means as well as confidence intervals, so we randomly selected one.
The "WordSim-cs-Multi.csv" file contains human judgments for all translation variants.
In both data sets, we preserved all 25 individual scores. In the WordSim353-cs data set, we added a column with their Czech means as well as a column containing the original English means and 95% confidence intervals in separate columns for each mean (computed by the CI function in the Rmisc R package). The WordSim-cs-Multi data set contains only the Czech means and confidence intervals. For the most convenient lexical search, we provided separate columns with the respective Czech and English single words, entire word pairs, and eventually an English-Czech quadruple in both data sets.
The data set also contains an xls table with the four translations and a preliminary selection of the best variants performed by an adjudicator.
Angabe von Wort, Anzahl, Häufigkeitsklasse, Beschreibung, Sachgebiet, Morphologie, Relationen zu anderen Wörtern (z. B. Synonymie), Links zu anderen Wörtern, Dornseiff-Bedeutungsgruppen, Beispielen (u.a. entnommen aus spiegel.de, sueddeutsche.de), signifikanten Kookkurenzen, signifikanten linken und rechten Nachbarn
Tool for designing and performing Word Sense Disambiguation (WSD) experiments. Current version (prototype) facilitates the construction and evaluation of WSD methods in the supervised Machine Learning paradigm.