This small dataset contains 3 speech corpora collected using the Alex Translate telephone service (https://ufal.mff.cuni.cz/alex#alex-translate).
The "part1" and "part2" corpora contain English speech with transcriptions and Czech translations. These recordings were collected from users of the service. Part 1 contains earlier recordings, filtered to include only clean speech; Part 2 contains later recordings with no filtering applied.
The "cstest" corpus contains recordings of artificially created sentences, each containing one or more Czech names of places in the Czech Republic. These were recorded by a multinational group of students studying in Prague.
We present a test corpus of audio recordings and transcriptions of presentations of students' enterprises together with their slides and web-pages. The corpus is intended for evaluation of automatic speech recognition (ASR) systems, especially in conditions where the prior availability of in-domain vocabulary and named entities is benefitable.
The corpus consists of 39 presentations in English, each up to 90 seconds long, and slides and web-pages in Czech, Slovak, English, German, Romanian, Italian or Spanish.
The speakers are high school students from European countries with English as their second language.
We benchmark three baseline ASR systems on the corpus and show their imperfection.
Additional three Czech reference translations of the whole WMT 2011 data set (http://www.statmt.org/wmt11/test.tgz), translated from the German originals. Original segmentation of the WMT 2011 data is preserved. and This project has been sponsored by the grants GAČR P406/11/1499 and EuroMatrixPlus (FP7-ICT-2007-3-231720 of the EU and 7E09003+7E11051 of the Ministry of Education, Youth and Sports of the Czech Republic)
Lexical network AdjDeriNet consists of pairs of base adjectives and their derivatives. It contains nearly 18 thousand base adjectives that are base words for more than 26 thousand lexemes of several parts of speech.
AKCES-GEC is a grammar error correction corpus for Czech generated from a subset of AKCES. It contains train, dev and test files annotated in M2 format.
Note that in comparison to CZESL-GEC dataset, this dataset contains separated edits together with their type annotations in M2 format and also has two times more sentences.
If you use this dataset, please use following citation:
@article{naplava2019wnut,
title={Grammatical Error Correction in Low-Resource Scenarios},
author={N{\'a}plava, Jakub and Straka, Milan},
journal={arXiv preprint arXiv:1910.00353},
year={2019}
}
A dataset intended for fully trainable natural language generation (NLG) systems in task-oriented spoken dialogue systems (SDS), covering the English public transport information domain. It includes preceding context (user utterance) along with each data instance (pair of source meaning representation and target natural language paraphrase to be generated).
Taking the form of the previous user utterance into account for generating the system response allows NLG systems trained on this dataset to entrain (adapt) to the preceding utterance, i.e., reuse wording and syntactic structure. This should presumably improve the perceived naturalness of the output, and may even lead to a higher task success rate.
Crowdsourcing has been used to obtain natural context user utterances as well as natural system responses to be generated.
We defined 58 dramatic situations and annotated them in 19 play scripts. Then we selected only 5 well-recognized dramatic situations and annotated further 33 play scripts. In this version of the data, we release only play scripts that can be freely distributed, which is 9 play scripts. One play is annotated independently by three annotators.
We defined 58 dramatic situations and annotated them in 19 play scripts. Then we selected only 5 well-recognized dramatic situations and annotated further 33 play scripts. In the previous (first) version, we released 9 play scripts that could be freely distributed. In this (second) version of the data, we are adding another 10 plays for which we have obtained licenses from authors. In total, there are 19 play scripts available, and one of them is annotated three times - independently by three annotators.
Artificially created treebank of elliptical constructions (gapping), in the annotation style of Universal Dependencies. Data taken from UD 2.1 release, and from large web corpora parsed by two parsers. Input data are filtered, sentences are identified where gapping could be applied, then those sentences are transformed, one or more words are omitted, resulting in a sentence with gapping. Details in Droganova et al.: Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions, LREC 2018, Miyazaki, Japan.
This dataset contains a number of user product reviews which are publicly available on the website of an established Czech online shop with electronic devices. Each review consists of negative and positive aspects of the product. This setting pushes the customer to rate important characteristics.
We have selected 2000 positive and negative segments from these reviews and manually tagged their targets. Additionally, we selected 200 of the longest reviews and annotated them in the same way. The targets were either aspects of the evaluated product or some general attributes (e.g. price, ease of use).
This record contains audio recordings of proceedings of the Chamber of Deputies of the Parliament of the Czech Republic. The recordings have been provided by the official websites of the Chamber of Deputies, and the set contains them in their original format with no further processing.
Recordings cover all available audio files from 2013-11-25 to 2023-07-26. Audio files are packed by year (2013-2023) and quarter (Q1-Q4) in tar archives audioPSP-YYYY-QN.tar.
Furthermore, there are two TSV files: audioPSP-meta.quarterArchive.tsv contains metadata about archives, and audioPSP-meta.audioFile.tsv contains metadata about individual audio files.
This dataset contains automatic paraphrases of Czech official reference translations for the Workshop on Statistical Machine Translation shared task. The data covers the years 2011, 2013 and 2014.
For each sentence, at most 10000 paraphrases were included (randomly selected from the full set).
The goal of using this dataset is to improve automatic evaluation of machine translation outputs.
If you use this work, please cite the following paper:
Tamchyna Aleš, Barančíková Petra: Automatic and Manual Paraphrases for MT Evaluation. In proceedings of LREC, 2016.
Automatically generated spelling correction corpus for Czech (Czesl-SEC-AG) is a corpus containg text with automatically generated spelling errors. To create spelling errors, a character error model containing probabilities of character substitution, insertion, deletion and probabilities of swaping two adjacent characters is used. Besides these probabilities, also the probabilities of changing character casing are considered. The original clean text on which the spelling errors were generated is PDT3.0 (http://hdl.handle.net/11858/00-097C-0000-0023-1AAF-3). The original train/dev/test sentence split of PDT3.0 corpus is preserved in this dataset.
Besides the data with artificial spelling errors, we also publish texts from which the character error model was created. These are the original manual transcript of an audiobook Švejk and its corrected version performed by authors of Korektor (http://ufal.mff.cuni.cz/korektor). These data are similarly to CzeSL Grammatical Error Correction Dataset (CzeSL-GEC: http://hdl.handle.net/11234/1-2143) processed into four sets based on error difficulty present.
Data
-------
Bengali Visual Genome (BVG for short) 1.0 has similar goals as Hindi Visual Genome (HVG) 1.1: to support the Bengali language. Bengali Visual Genome 1.0 is the multi-modal dataset in Bengali for machine translation and image
captioning. Bengali Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Bengali multimodal machine translation tasks and multimodal research. We follow the same selection of short English segments (captions) and the associated images from Visual Genome as HGV 1.1 has. For BVG, we manually translated these captions from English to Bengali taking the associated images into account. The manual translation is performed by the native Bengali speakers without referring to any machine translation system.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome. A third test set is
called the ``challenge test set'' and consists of 1.4K segments. The challenge test set was created for the WAT2019 multi-modal task by searching for (particularly) ambiguous English words based on the embedding similarity and
manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word.
Dataset Formats
---------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Bengali Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
---------------
The statistics of the current release are given below.
Parallel Corpus Statistics
--------------------------
Dataset Segments English Words Bengali Words
---------- -------- ------------- -------------
Train 28930 143115 113978
Dev 998 4922 3936
Test 1595 7853 6408
Challenge Test 1400 8186 6657
---------- -------- ------------- -------------
Total 32923 164076 130979
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@inproceedings{hindi-visual-genome:2022,
title= "{Bengali Visual Genome: A Multimodal Dataset for Machine Translation and Image Captioning}",
author={Sen, Arghyadeep
and Parida, Shantipriya
and Kotwal, Ketan
and Panda, Subhadarshi
and Bojar, Ond{\v{r}}ej
and Dash, Satya Ranjan},
editor={Satapathy, Suresh Chandra
and Peer, Peter
and Tang, Jinshan
and Bhateja, Vikrant
and Ghosh, Anumoy},
booktitle= {Intelligent Data Engineering and Analytics},
publisher= {Springer Nature Singapore},
address= {Singapore},
pages = {63--70},
isbn = {978-981-16-6624-7},
doi = {10.1007/978-981-16-6624-7_7},
}
Relationship extraction models for the Czech language. Models are trained on CERED (dataset created by distant supervision on Czech Wikipedia and Wikidata) and recognize a subset of Wikidata relations (listed in CEREDx.LABELS).
We supply a demo.py that performs inference on user-defined input and requirements.txt file for pip. Adapt the demo code to use the model.
Both the dataset and the models are presented in Relationship Extraction thesis.
Czech data - both train and test+eval sets, as well as the valency dictionary - for the CoNLL 2009 Shared Task. Documentation is included. The data are generated from PDT 2.0. LDC catalog number: LDC2009E34B and MSM 0021620838 (http://ufal.mff.cuni.cz:8080/bib/?section=grant&id=116488695895567&mode=view)
Czech trial (example) data for CoNLL 2009 Shared Task. The data are generated from PDT 2.0. LDC2009E32B and MSM 0021620838 (http://ufal.mff.cuni.cz:8080/bib/?section=grant&id=116488695895567&mode=view)
CoNLL 2017 and 2018 shared tasks:
Multilingual Parsing from Raw Text to Universal Dependencies
This package contains the test data in the form in which they ware presented
to the participating systems: raw text files and files preprocessed by UDPipe.
The metadata.json files contain lists of files to process and to output;
README files in the respective folders describe the syntax of metadata.json.
For full training, development and gold standard test data, see
Universal Dependencies 2.0 (CoNLL 2017)
Universal Dependencies 2.2 (CoNLL 2018)
See the download links at http://universaldependencies.org/.
For more information on the shared tasks, see
http://universaldependencies.org/conll17/
http://universaldependencies.org/conll18/
Contents:
conll17-ud-test-2017-05-09 ... CoNLL 2017 test data
conll18-ud-test-2018-05-06 ... CoNLL 2018 test data
conll18-ud-test-2018-05-06-for-conll17 ... CoNLL 2018 test data with metadata
and filenames modified so that it is digestible by the 2017 systems.
Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/).
For each language, automatic annotations in CoNLL-U format are provided in a separate archive. The word embeddings for all languages are distributed in one archive.
Note that the CC BY-SA-NC 4.0 license applies to the automatically generated annotations and word embeddings, not to the underlying data, which may have different license and impose additional restrictions.
Update 2018-09-03
===============
Added data in the 4 “surprise languages” from the 2017 ST: Buryat, Kurmanji, North Sami and Upper Sorbian. This has been promised before, during CoNLL-ST 2018 we gave the participants a link to this record saying the data was here. It wasn't, sorry. But now it is.
Baseline UDPipe models for CoNLL 2017 Shared Task in UD Parsing, and supplementary material.
The models require UDPipe version at least 1.1 and are evaluated using the official evaluation script.
The models are trained on a slightly different split of the official UD 2.0 CoNLL 2017 training data, so called baselinemodel split, in order to allow comparison of models even during the shared task. This baselinemodel split of UD 2.0 CoNLL 2017 training data is available for download.
Furthermore, we also provide UD 2.0 CoNLL 2017 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data.
Finally, we supply all required data and hyperparameter values needed to replicate the baseline models.
Baseline UDPipe models for CoNLL 2018 Shared Task in UD Parsing, and supplementary material.
The models require UDPipe version at least 1.2 and are evaluated using the official evaluation script. The models were trained using a custom data split for treebanks where no development data is provided. Also, we trained an additional "Mixed" model, which uses 200 sentences from every training data. All information needed to replicate the model training (hyperparameters, modified train-dev split, and pre-computed word embeddings for the parser) are included in the archive.
Additionaly, we provide UD 2.2 CoNLL 2018 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data.
CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 0.1 consists of 17 datasets for 11 languages.
The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column.
The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 13 datasets for 10 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 1 for Hungarian, 1 for Lithuanian, 1 for Polish, 1 for Russian, and 1 for Spanish), excluding the test data.
The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets.
When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource too.
References to original resources whose harmonized versions are contained in the public edition of CorefUD 0.1:
- Catalan-AnCora:
Recasens, M. and Martí, M. A. (2010). AnCora-CO: Coreferentially Annotated Corpora for Spanish and Catalan. Language Resources and Evaluation, 44(4):315–345
- Czech-PCEDT:
Nedoluzhko, A., Novák, M., Cinková, S., Mikulová, M., and Mírovský, J. (2016). Coreference in Prague Czech-English Dependency Treebank. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 169–176, Portorož, Slovenia. European Language Resources Association.
- Czech-PDT:
Hajič, J., Bejček, E., Hlaváčová, J., Mikulová, M., Straka, M., Štěpánek, J., and Štěpánková, B. (2020). Prague Dependency Treebank - Consolidated 1.0. In Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020), pages 5208–5218, Marseille, France. European Language Resources Association.
- English-GUM:
Zeldes, A. (2017). The GUM Corpus: Creating Multilayer Resources in the Classroom. Language Resources and Evaluation, 51(3):581–612.
- English-ParCorFull:
Lapshinova-Koltunski, E., Hardmeier, C., and Krielke, P. (2018). ParCorFull: a Parallel Corpus Annotated with Full Coreference. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association.
- French-Democrat:
Landragin, F. (2016). Description, modélisation et détection automatique des chaı̂nes de référence (DEMOCRAT). Bulletin de l’Association Française pour l’Intelligence Artificielle, (92):11–15.
- German-ParCorFull:
Lapshinova-Koltunski, E., Hardmeier, C., and Krielke, P. (2018). ParCorFull: a Parallel Corpus Annotated with Full Coreference. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association
- German-PotsdamCC:
Bourgonje, P. and Stede, M. (2020). The Potsdam Commentary Corpus 2.2: Extending annotations for shallow discourse parsing. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1061–1066, Marseille, France. European Language Resources Association.
- Hungarian-SzegedKoref:
Vincze, V., Hegedűs, K., Sliz-Nagy, A., and Farkas, R. (2018). SzegedKoref: A Hungarian Coreference Corpus. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association.
- Lithuanian-LCC:
Žitkus, V. and Butkienė, R. (2018). Coreference Annotation Scheme and Corpus for Lithuanian Language. In Fifth International Conference on Social Networks Analysis, Management and Security, SNAMS 2018, Valencia, Spain, October 15-18, 2018, pages 243–250. IEEE.
- Polish-PCC:
Ogrodniczuk, M., Glowińska, K., Kopeć, M., Savary, A., and Zawisławska, M. (2013). Polish coreference corpus. In Human Language Technology. Challenges for Computer Science and Linguistics - 6th Language and Technology Conference, LTC 2013, Poznań, Poland, December 7-9, 2013. Revised Selected Papers, volume 9561 of Lecture Notes in Computer Science, pages 215–226. Springer.
- Russian-RuCor:
Toldova, S., Roytberg, A., Ladygina, A. A., Vasilyeva, M. D., Azerkovich, I. L., Kurzukov,M., Sim, G., Gorshkov, D. V., Ivanova, A., Nedoluzhko, A., and Grishina, Y. (2014). Evaluating Anaphora and Coreference Resolution for Russian. In Komp’juternaja lingvistika i intellektual’nye tehnologii. Po materialam ezhegodnoj Mezhdunarodnoj konferencii
Dialog, pages 681–695.
- Spanish-AnCora:
Recasens, M. and Martí, M. A. (2010). AnCora-CO: Coreferentially Annotated Corpora for Spanish and Catalan. Language Resources and Evaluation, 44(4):315–345
References to original resources whose harmonized versions are contained in the ÚFAL-internal edition of CorefUD 0.1:
- Dutch-COREA:
Hendrickx, I., Bouma, G., Coppens, F., Daelemans, W., Hoste, V., Kloosterman, G., Mineur, A.-M., Van Der Vloet, J., and Verschelde, J.-L. (2008). A coreference corpus and resolution system for Dutch. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco. European Language Resources Association.
- English-ARRAU:
Uryupina, O., Artstein, R., Bristot, A., Cavicchio, F., Delogu, F., Rodriguez, K. J., and Poesio, M. (2020). Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus. Natural Language Engineering, 26(1):95–128.
- English-OntoNotes:
Weischedel, R., Hovy, E., Marcus, M., Palmer, M., Belvin, R., Pradhan, S., Ramshaw, L., and Xue, N. (2011). Ontonotes: A large training corpus for enhanced processing. In Handbook of Natural Language Processing and Machine Translation: DARPA Global Autonomous Language Exploitation, pages 54–63, New York. Springer-Verlag.
- English-PCEDT:
Nedoluzhko, A., Novák, M., Cinková, S., Mikulová, M., and Mírovský, J. (2016). Coreference in Prague Czech-English Dependency Treebank. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 169–176, Portorož, Slovenia. European Language Resources Association.
CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 0.2 consists of 17 datasets for 11 languages.
The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column.
The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 13 datasets for 10 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 1 for Hungarian, 1 for Lithuanian, 1 for Polish, 1 for Russian, and 1 for Spanish), excluding the test data.
The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets.
When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource too.
Version 0.2 consists of exactly the same datasets as the version 0.1. All automatically parsed datasets were re-parsed for v0.2 using UDPipe 2 with models trained on UD 2.6. Catalan-AnCora, Spanish-AnCora and English-GUM have been updated to match the their UD 2.9 versions.
CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 1.0 consists of 17 datasets for 11 languages. The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column. The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 13 datasets for 10 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 1 for Hungarian, 1 for Lithuanian, 1 for Polish, 1 for Russian, and 1 for Spanish), excluding the test data. The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets. When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource too. Version 1.0 consists of the same corpora and languages as the previous version 0.2; however, the English GUM dataset has been updated to a newer and larger version, and in the Czech/English PCEDT dataset, the train-dev-test split has been changed to be compatible with OntoNotes. Nevertheless, the main change is in the file format (the MISC attributes have new form and interpretation).
CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 1.1 consists of 21 datasets for 13 languages. The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column. The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 17 datasets for 12 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 2 for Hungarian, 1 for Lithuanian, 2 for Norwegian, 1 for Polish, 1 for Russian, 1 for Spanish, and 1 for Turkish), excluding the test data. The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets. When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource too. Compared to the previous version 1.0, the version 1.1 comprises new languages and corpora, namely Hungarian-KorKor, Norwegian-BokmaalNARC, Norwegian-NynorskNARC, and Turkish-ITCC. In addition, the English GUM dataset has been updated to a newer and larger version, and the conversion pipelines for most datasets have been refined (a list of all changes in each dataset can be found in the corresponding README file).
CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 1.2 consists of 25 datasets for 16 languages. The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column. The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 21 datasets for 15 languages (1 dataset for Ancient Greek, 1 for Ancient Hebrew, 1 for Catalan, 2 for Czech, 3 for English, 1 for French, 2 for German, 2 for Hungarian, 1 for Lithuanian, 2 for Norwegian, 1 for Old Church Slavonic, 1 for Polish, 1 for Russian, 1 for Spanish, and 1 for Turkish), excluding the test data. The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets. When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource, too. Compared to the previous version 1.1, the version 1.2 comprises new languages and corpora, namely Ancient_Greek-PROIEL, Ancient_Hebrew-PTNK, English-LitBank, and Old_Church_Slavonic-PROIEL. In addition, English-GUM and Turkish-ITCC have been updated to newer versions, conversion of zeros in Polish-PCC has been improved, and the conversion pipelines for multiple other datasets have been refined (a list of all changes in each dataset can be found in the corresponding README file).
The `corpipe23-corefud1.1-231206` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 23 (https://github.com/ufal/crac2023-corpipe). It is released under the CC BY-NC-SA 4.0 license.
The model is language agnostic (no _corpus id_ on input), so it can be used to predict coreference in any `mT5` language (for zero-shot evaluation, see the paper). However, note that the empty nodes must be present already on input, they are not predicted (the same settings as in the CRAC23 shared task).
Corpus of texts in 12 languages. For each language, we provide one training, one development and one testing set acquired from Wikipedia articles. Moreover, each language dataset contains (substantially larger) training set collected from (general) Web texts. All sets, except for Wikipedia and Web training sets that can contain similar sentences, are disjoint. Data are segmented into sentences which are further word tokenized.
All data in the corpus contain diacritics. To strip diacritics from them, use Python script diacritization_stripping.py contained within attached stripping_diacritics.zip. This script has two modes. We generally recommend using method called uninames, which for some languages behaves better.
The code for training recurrent neural-network based model for diacritics restoration is located at https://github.com/arahusky/diacritics_restoration.
COSTRA 1.0 is a dataset of Czech complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing.
The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation.
The hope is that with this dataset, we should be able to test semantic properties of sentence embeddings and perhaps even to find some topologically interesting “skeleton” in the sentence embedding space.
Costra 1.1 is a new dataset for testing geometric properties of sentence embeddings spaces. In particular, it concentrates on examining how well sentence embeddings capture complex phenomena such paraphrases, tense or generalization. The dataset is a direct expansion of Costra 1.0, which was extended with more sentences and sentence comparisons.
CsEnVi Pairwise Parallel Corpora consist of Vietnamese-Czech parallel corpus and Vietnamese-English parallel corpus. The corpora were assembled from the following sources:
- OPUS, the open parallel corpus is a growing multilingual corpus of translated open source documents.
The majority of Vi-En and Vi-Cs bitexts are subtitles from movies and television series.
The nature of the bitexts are paraphrasing of each other's meaning, rather than translations.
- TED talks, a collection of short talks on various topics, given primarily in English, transcribed and with transcripts translated to other languages. In our corpus, we use 1198 talks which had English and Vietnamese transcripts available and 784 talks which had Czech and Vietnamese transcripts available in January 2015.
The size of the original corpora collected from OPUS and TED talks is as follows:
CS/VI EN/VI
Sentence 1337199/1337199 2035624/2035624
Word 9128897/12073975 16638364/17565580
Unique word 224416/68237 91905/78333
We improve the quality of the corpora in two steps: normalizing and filtering.
In the normalizing step, the corpora are cleaned based on the general format of subtitles and transcripts. For instance, sequences of dots indicate explicit continuation of subtitles across multiple time frames. The sequences of dots are distributed differently in the source and the target side. Removing the sequence of dots, along with a number of other normalization rules, improves the quality of the alignment significantly.
In the filtering step, we adapt the CzEng filtering tool [1] to filter out bad sentence pairs.
The size of cleaned corpora as published is as follows:
CS/VI EN/VI
Sentence 1091058/1091058 1113177/1091058
Word 6718184/7646701 8518711/8140876
Unique word 195446/59737 69513/58286
The corpora are used as training data in [2].
References:
[1] Ondřej Bojar, Zdeněk Žabokrtský, et al. 2012. The Joy of Parallelism with CzEng 1.0. Proceedings of LREC2012. ELRA. Istanbul, Turkey.
[2] Duc Tam Hoang and Ondřej Bojar, The Prague Bulletin of Mathematical Linguistics. Volume 104, Issue 1, Pages 75–86, ISSN 1804-0462. 9/2015
CUBBITT En-Cs 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 newstest2014 (BLEU):
en->cs: 27.6
cs->en: 34.4
(Evaluated using multeval: https://github.com/jhclark/multeval)
CUBBITT En-Fr 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 newstest2014 (BLEU):
en->fr: 38.2
fr->en: 36.7
(Evaluated using multeval: https://github.com/jhclark/multeval)
CUBBITT En-Pl 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->pl: 12.3
pl->en: 20.0
(Evaluated using multeval: https://github.com/jhclark/multeval)
Web corpus of Czech, created in 2011. Contains newspapers+magazines, discussions, blogs. See http://www.lrec-conf.org/proceedings/lrec2012/summaries/120.html for details. and GA405/09/0278
This is a document-aligned parallel corpus of English and Czech abstracts of scientific papers published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague, as reported in the institute's system Biblio. For each publication, the authors are obliged to provide both the original abstract in Czech or English, and its translation into English or Czech, respectively. No filtering was performed, except for removing entries missing the Czech or English abstract, and replacing newline and tabulator characters by spaces.
This is a parallel corpus of Czech and mostly English abstracts of scientific papers and presentations published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague. For each publication record, the authors are obliged to provide both the original abstract (in Czech or English), and its translation (English or Czech) in the internal Biblio system. The data was filtered for duplicates and missing entries, ensuring that every record is bilingual. Additionally, records of published papers which are indexed by SemanticScholar contain the respective link. The dataset was created from September 2022 image of the Biblio database and is stored in JSONL format, with each line corresponding to one record.
We present the Czech Court Decisions Dataset (CCDD) -- a dataset of 300 manually annotated court decisions published by The Supreme Court of the Czech Republic and the Constitutional Court of the Czech Republic.
Czech Contracts dataset was created as a part of the thesis Low-resource Text Classification (2021), A. Szabó, MFF UK.
Contracts are obtained from the Hlídač Státu web portal. Labels in the development and training set are automatically classified on the basis of the keyword method according to the thesis Automatická klasifikace smluv pro portál HlidacSmluv.cz, J. Maroušek (2020), MFF UK. For this reason, the goal in the classification is not to achieve 100% on the development set, as the classification contains a certain amount of noise. The test set is manually annotated. The dataset contains a total of 97493 contracts.
This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving three NLP tasks: machine translation, image captioning, and sentiment analysis.
The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks.
The models are described in the accompanying paper.
The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd
There are several separate ZIP archives here, each containing one model solving one of the tasks for one language.
To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey
To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory.
Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization).
The 'experiment.ini' file, which was used to train the model, is also included.
Then there are files containing the model itself, files containing the input and output vocabularies, etc.
For the sentiment analyzers, you should tokenize your input data using the Moses tokenizer: https://pypi.org/project/mosestokenizer/
For the machine translation, you do not need to tokenize the data, as this is done by the model.
For image captioning, you need to:
- download a trained ResNet: http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
- clone the git repository with TensorFlow models: https://github.com/tensorflow/models
- preprocess the input images with the Neural Monkey 'scripts/imagenet_features.py' script (https://github.com/ufal/neuralmonkey/blob/master/scripts/imagenet_features.py) -- you need to specify the path to ResNet and to the TensorFlow models to this script
Feel free to contact the authors of this submission in case you run into problems!
This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving four NLP tasks: machine translation, image captioning, sentiment analysis, and summarization.
The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks.
The models are described in the accompanying paper.
The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd
In addition to the models presented in the referenced paper (developed and published in 2018), we include models for automatic news summarization for Czech and English developed in 2019. The Czech models were trained using the SumeCzech dataset (https://www.aclweb.org/anthology/L18-1551.pdf), the English models were trained using the CNN-Daily Mail corpus (https://arxiv.org/pdf/1704.04368.pdf) using the standard recurrent sequence-to-sequence architecture.
There are several separate ZIP archives here, each containing one model solving one of the tasks for one language.
To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey
To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory.
Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization).
The 'experiment.ini' file, which was used to train the model, is also included.
Then there are files containing the model itself, files containing the input and output vocabularies, etc.
For the sentiment analyzers, you should tokenize your input data using the Moses tokenizer: https://pypi.org/project/mosestokenizer/
For the machine translation, you do not need to tokenize the data, as this is done by the model.
For image captioning, you need to:
- download a trained ResNet: http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
- clone the git repository with TensorFlow models: https://github.com/tensorflow/models
- preprocess the input images with the Neural Monkey 'scripts/imagenet_features.py' script (https://github.com/ufal/neuralmonkey/blob/master/scripts/imagenet_features.py) -- you need to specify the path to ResNet and to the TensorFlow models to this script
The summarization models require input that is tokenized with Moses Tokenizer (https://github.com/alvations/sacremoses) and lower-cased.
Feel free to contact the authors of this submission in case you run into problems!
The Czech Legal Text Treebank (CLTT) is a collection of 1133 manually annotated dependency trees. CLTT consists of two legal documents: The Accounting Act (563/1991 Coll., as amended) and Decree on Double-entry Accounting for undertakers (500/2002 Coll., as amended).
The Czech Legal Text Treebank 2.0 (CLTT 2.0) annotates the same texts as the CLTT 1.0. These texts come from the legal domain and they are manually syntactically annotated. The CLTT 2.0 annotation on the syntactic layer is more elaborate than in the CLTT 1.0 from various aspects. In addition, new annotation layers were added to the data: (i) the layer of accounting entities, and (ii) the layer of semantic entity relations.
The package contains Czech recordings of the Visual History Archive which consists of the interviews with the Holocaust survivors. The archive consists of audio recordings, four types of automatic transcripts, manual annotations of selected topics and interviews' metadata. The archive totally contains 353 recordings and 592 hours of interviews.
Czech models for NameTag, providing recognition of named entities.
The models are trained on Czech Named Entity Corpus 2.0 and 1.1. and This work has been using language resources developed and/or stored and/or distributed by the LINDAT/CLARIN project of the Ministry of Education of the Czech Republic (project LM2010013).
Czech models are trained on Czech Named Entity Corpus, which was created by Magda Ševčíková, Zdeněk Žabokrtský, Jana Straková and Milan Straka.
The recognizer research was supported by the projects MSM0021620838 and LC536 of Ministry of Education, Youth and Sports of the Czech Republic, 1ET101120503 of Academy of Sciences of the Czech Republic, LINDAT/CLARIN project of the Ministry of Education of the Czech Republic (project LM2010013), and partially by SVV project number 267 314. The research was performed by Jana Straková, Zdeněk Žabokrtský and Milan Straka.
Czech models use MorphoDiTa as a tagger and lemmatizer, therefore MorphoDiTa Acknowledgements (http://ufal.mff.cuni.cz/morphodita#morphodita_acknowledgements) and Czech MorphoDiTa Model Acknowledgements (http://ufal.mff.cuni.cz/morphodita/users-manual#czech-morfflex-pdt_acknowledgements) apply.
Czech models for MorphoDiTa, providing morphological analysis, morphological generation and part-of-speech tagging.
The morphological dictionary is created from MorfFlex CZ and the PoS tagger is trained on PDT (Prague Dependency Treebank). and This work has been using language resources developed and/or stored and/or distributed by the LINDAT/CLARIN project of the Ministry of Education of the Czech Republic (project LM2010013).
The Czech morphologic system was devised by Jan Hajič.
The MorfFlex CZ dictionary was created by Jan Hajič and Jaroslava Hlaváčová.
The morphologic guesser research was supported by the projects 1ET101120503 and 1ET101120413 of Academy of Sciences of the Czech Republic and 100008/2008 of Charles University Grant Agency. The research was performed by Jan Hajič, Jaroslava Hlaváčová and David Kolovratník.
The tagger algorithm and feature set research was supported by the projects MSM0021620838 and LC536 of Ministry of Education, Youth and Sports of the Czech Republic, GA405/09/0278 of the Grant Agency of the Czech Republic and 1ET101120503 of Academy of Sciences of the Czech Republic. The research was performed by Drahomíra "johanka" Spoustová, Jan Hajič, Jan Raab and Miroslav Spousta.
The tagger is trained on morphological layer of Prague Dependency Treebank PDT 2.5, which was supported by the projects LM2010013, LC536, LN00A063 and MSM0021620838 of Ministry of Education, Youth and Sports of the Czech Republic, and developed by Martin Buben, Jan Hajič, Jiří Hana, Hana Hanová, Barbora Hladká, Emil Jeřábek, Lenka Kebortová, Kristýna Kupková, Pavel Květoň, Jiří Mírovský, Andrea Pfimpfrová, Jan Štěpánek and Daniel Zeman.