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.
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.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
AGREE is a dataset and task for evaluation of language models based on grammar agreement in Czech. The dataset consists of sentences with marked suffixes of past tense verbs. The task is to choose the right verb suffix which depends on gender, number and animacy of subject. It is challenging for language models because 1) Czech is morphologically rich, 2) it has relatively free word order, 3) high out-of-vocabulary (OOV) ratio, 4) predicate and subject can be far from each other, 5) subjects can be unexpressed and 6) various semantic rules may apply. The task provides a straightforward and easily reproducible way of evaluating language models on a morphologically rich language.
The dataset contains two parts: the original Stanford Natural Language Inference (SNLI) dataset with automatic translations to Czech, for some items from the SNLI, it contains annotation of the Czech content and explanation.
The Czech SNLI data contain both Czech and English pairs premise-hypothesis. SNLI split into train/test/dev is preserved.
- CZtrainSNLI.csv: 550152 pairs
- CZtestSNLI.csv: 10000 pairs
- CZdevSNLI.csv: 10000 pairs
The explanation dataset contains batches of pairs premise-hypothesis. Each batch contains 1499 pairs. Each pair contains:
- reference to original SNLI example
- English premise and English hypothesis
- English gold label (one of Entailment, Contradiction, Neutral)
- automatically translated premise and hypothesis to Czech
- Czech gold label (one of entailment, contradiction, neutral, bad translation)
- explanations for Czech label
Example record:
CSNLI ID: 4857558207.jpg#4r1e
English premise: A mother holds her newborn baby.
English hypothesis: A person holding a child.
English gold label: entailment
Czech premise: Matka drží své novorozené dítě.
Czech hypothesis: Osoba, která drží dítě.
Czech gold label: Entailment
Explanation-hypothesis: Matka
Explanation-premise: Osoba
Explanation-relation: generalization
Size of the explanations dataset:
- train: 159650
- dev: 2860
- test: 2880
Inter-Annotator Agreement (IAA)
Packages 1 and 12 annotate the same data. The IAA measured by the kappa score is 0.67 (substantial agreement).
The translation was performed via LINDAT translation service.
Next, the translated pairs were manually checked (without access to the original English gold label), with possible check of the original pair.
Explanations were annotated as follows:
- if there is a part of the premise or hypothesis that is relevant for the annotator's decision, it is marked
- if there are two such parts and there exists a relation between them, the relation is marked
Possible relation types:
- generalization: white long skirt - skirt
- specification: dog - bulldog
- similar: couch - sofa
- independence: they have no instruments - they belong to the group
- exclusion: man - woman
Original SNLI dataset: https://nlp.stanford.edu/projects/snli/
LINDAT Translation Service: https://lindat.mff.cuni.cz/services/translation/
This is a dataset for natural language generation (NLG) in task-oriented spoken dialogue systems with Czech as the target language. It originated as a translation of the English San Francisco Restaurants dataset by Wen et al. (2015).
It includes input dialogue acts and the corresponding output natural language paraphrases in Czech. Since the dataset is intended for recurrent neural network based NLG systems using delexicalization, inflection tables for all slot values appearing verbatim in the text are provided.
We present DaMuEL, a large Multilingual Dataset for Entity Linking containing data in 53 languages. DaMuEL consists of two components: a knowledge base that contains language-agnostic information about entities, including their claims from Wikidata and named entity types (PER, ORG, LOC, EVENT, BRAND, WORK_OF_ART, MANUFACTURED); and Wikipedia texts with entity mentions linked to the knowledge base, along with language-specific text from Wikidata such as labels, aliases, and descriptions, stored separately for each language. The Wikidata QID is used as a persistent, language-agnostic identifier, enabling the combination of the knowledge base with language-specific texts and information for each entity. Wikipedia documents deliberately annotate only a single mention for every entity present; we further automatically detect all mentions of named entities linked from each document. The dataset contains 27.9M named entities in the knowledge base and 12.3G tokens from Wikipedia texts. The dataset is published under the CC BY-SA licence.
Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).