Tools and scripts used to create the cross-lingual parsing models submitted to VarDial 2017 shared task (https://bitbucket.org/hy-crossNLP/vardial2017), as described in the linked paper. The trained UDPipe models themselves are published in a separate submission (https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1971).
For each source (SS, e.g. sl) and target (TT, e.g. hr) language,
you need to add the following into this directory:
- treebanks (Universal Dependencies v1.4):
SS-ud-train.conllu
TT-ud-predPoS-dev.conllu
- parallel data (OpenSubtitles from Opus):
OpenSubtitles2016.SS-TT.SS
OpenSubtitles2016.SS-TT.TT
!!! If they are originally called ...TT-SS... instead of ...SS-TT...,
you need to symlink them (or move, or copy) !!!
- target tagging model
TT.tagger.udpipe
All of these can be obtained from https://bitbucket.org/hy-crossNLP/vardial2017
You also need to have:
- Bash
- Perl 5
- Python 3
- word2vec (https://code.google.com/archive/p/word2vec/); we used rev 41 from 15th Sep 2014
- udpipe (https://github.com/ufal/udpipe); we used commit 3e65d69 from 3rd Jan 2017
- Treex (https://github.com/ufal/treex); we used commit d27ee8a from 21st Dec 2016
The most basic setup is the sl-hr one (train_sl-hr.sh):
- normalization of deprels
- 1:1 word-alignment of parallel data with Monolingual Greedy Aligner
- simple word-by-word translation of source treebank
- pre-training of target word embeddings
- simplification of morpho feats (use only Case)
- and finally, training and evaluating the parser
Both da+sv-no (train_ds-no.sh) and cs-sk (train_cs-sk.sh) add some cross-tagging, which seems to be useful only in
specific cases (see paper for details).
Moreover, cs-sk also adds more morpho features, selecting those that
seem to be very often shared in parallel data.
The whole pipeline takes tens of hours to run, and uses several GB of RAM, so make sure to use a powerful computer.
Slovak Dependency Treebank (Slovenský závislostný korpus) was created as part of the Slovak National Corpus at the Ľ. Štúr Institute of the Slovak Academy of Sciences. The annotation follows the guidelines of the Prague Dependency Treebank (Czech), slightly modified in the spirit of Slovak grammatical tradition. Morphological tags, lemmas and dependency relations have been assigned manually to every word.
The present dataset is a subset of the original treebank. We automatically selected the sentences where the two human annotators 100% agreed on the analysis. This increases the quality and trustworthiness of the data but it also results in selecting short sentences most of the time. An extended version may be published in the future when manually merged and checked annotation is available.
The selected sentences have been converted to the CoNLL-X file format (original token IDs are preserved in the FEATS column). This PDT-style annotation will serve as the source for the first Slovak dataset in the Universal Dependencies (to be published separately).
Slovak models for MorphoDiTa, providing morphological analysis, morphological generation and part-of-speech tagging.
The morphological dictionary is created from MorfFlex SK 170914 and the PoS tagger is trained on automatically translated Prague Dependency Treebank 3.0 (PDT).
SMOR is a wide-coverage German computational morphology with inflection, derivation, and compounding. The SMOR code excepted the stem lexicon are available under the GNU license. SMOR (without a stem lexicon) comes with the SFST tools.
The dataset with 409,679 images belonging to 772 snake species from 188 countries and all continents (386,006 images with labels targeted for development and 23,673 images without labels for testing). In addition, we provide a simple train/val (90% / 10%) split to validate preliminary results while ensuring the same species distributions. Furthermore, we prepared a compact subset (70,208 images) for fast prototyping. The test set data consists of 23,673 images submitted to the iNaturalist platform within the "first four months of 2021.
All data were gathered from online biodiversity platforms (i.e., iNaturalist, HerpMapper) and further extended by data scraped from Flickr. The provided dataset has a heavy long-tailed class distribution, where the most frequent species (Thamnophis sirtalis) is represented by 22,163 images and the least frequent by just 10 (Achalinus formosanus).