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 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.