The original SDP 2014 and 2015 data collections were made available under task-specific ‘evaluation’ licenses to registered SemEval participants. In mid-2016, all original data has been bundled with system submissions, supporting software, an additional SDP-style collection of semantic dependency graphs, and additional background material (from which some of the SDP target representations were derived) for release through the Linguistic Data Consortium (with LDC catalogue number LDC2016 T10).
One of the four English target representations (viz. DM) and the entire Czech data (in the PSD target representation) are not derivative of LDC-licensed annotations and, thus, can be made available for direct download (Open SDP; version 1.2; January 2017) under a more permissive licensing scheme, viz. the Creative Common Attribution-NonCommercial-ShareAlike license. This package also includes some ‘richer’ meaning representations from which the English bi-lexical DM graphs derive, viz. scope-underspecified logical forms and more abstract, non-lexicalized ‘semantic networks’. The latter of these are formally (if not linguistically) similar to Abstract Meaning Representation (AMR) and are available in a range of serializations, including in AMR-like syntax.
Version 1.1 was released April 2016. Version 1.2 adds the 2015 Turku system, which was accidentally left out from version 1.1.
Please use the following bibliographic reference for the SDP 2016 data:
@string{C:LREC = {{I}nternational {C}onference on
{L}anguage {R}esources and {E}valuation}}
@string{LREC:16 = {Proceedings of the 10th } # C:LREC}
@string{L:LREC:16 = {Portoro\v{z}, Slovenia}}
@inproceedings{Oep:Kuh:Miy:16,
author = {Oepen, Stephan and Kuhlmann, Marco and Miyao, Yusuke
and Zeman, Daniel and Cinkov{\'a}, Silvie
and Flickinger, Dan and Haji\v{c}, Jan
and Ivanova, Angelina and Ure\v{s}ov{\'a}, Zde\v{n}ka},
title = {Towards Comparability of Linguistic Graph Banks for Semantic Parsing},
booktitle = LREC:16
year = 2016,
address = L:LREC:16,
pages = {3991--3995}
}
We define "optimal reference translation" as a translation thought to be the best possible that can be achieved by a team of human translators. Optimal reference translations can be used in assessments of excellent machine translations.
We selected 50 documents (online news articles, with 579 paragraphs in total) from the 130 English documents included in the WMT2020 news test (http://www.statmt.org/wmt20/) with the aim to preserve diversity (style, genre etc.) of the selection. In addition to the official Czech reference translation provided by the WMT organizers (P1), we hired two additional translators (P2 and P3, native Czech speakers) via a professional translation agency, resulting in three independent translations. The main contribution of this dataset are two additional translations (i.e. optimal reference translations N1 and N2), done jointly by two translators-cum-theoreticians with an extreme care for various aspects of translation quality, while taking into account the translations P1-P3. We publish also internal comments (in Czech) for some of the segments.
Translation N1 should be closer to the English original (with regards to the meaning and linguistic structure) and female surnames use the Czech feminine suffix (e.g. "Mai" is translated as "Maiová"). Translation N2 is more free, trying to be more creative, idiomatic and entertaining for the readers and following the typical style used in Czech media, while still preserving the rules of functional equivalence. Translation N2 is missing for the segments where it was not deemed necessary to provide two alternative translations. For applications/analyses needing translation of all segments, this should be interpreted as if N2 is the same as N1 for a given segment.
We provide the dataset in two formats: OpenDocument spreadsheet (odt) and plain text (one file for each translation and the English original). Some words were highlighted using different colors during the creation of optimal reference translations; this highlighting and comments are present only in the odt format (some comments refer to row numbers in the odt file). Documents are separated by empty lines and each document starts with a special line containing the document name (e.g. "# upi.205735"), which allows alignment with the original WMT2020 news test. For the segments where N2 translations are missing in the odt format, the respective N1 segments are used instead in the plain-text format.
Preamble 1.0 is a multilingual annotated corpus of the preamble of the EU REGULATION 2020/2092 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL. The corpus consists of four language versions of the preamble (Czech, English, French, Polish), each of them annotated with sentence subjects.
The data were annotated in the Brat tool (https://brat.nlplab.org/) and are distributed in the Brat native format, i.e. each annotated preamble is represented by the original plain text and a stand-off annotation file.
The dataset used for the Ptakopět experiment on outbound machine translation. It consists of screenshots of web forms with user queries entered. The queries are available also in a text form. The dataset comprises two language versions: English and Czech. Whereas the English version has been fully post-processed (screenshots cropped, queries within the screenshots highlighted, dataset split based on its quality etc.), the Czech version is raw as it was collected by the annotators.
The SynSemClass synonym verb lexicon is a result of a project investigating semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English language resources, i.e., relating verb meanings with respect to contextually-based verb synonymy. The lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (http://verbs.colorado.edu/verbnet/index.html), PropBank (http://verbs.colorado.edu/%7Empalmer/projects/ace.html), Ontonotes (http://verbs.colorado.edu/html_groupings/), and English Wordnet (https://wordnet.princeton.edu/). Part of the dataset are files reflecting interannotator agreement.
The ACL RD-TEC 2.0 has been developed with the aim of providing a benchmark for the evaluation of methods for terminology extraction and classification as well as entity recognition tasks based on specialised text from the computational linguistics domain. This release of the corpus consists of 300 abstracts from articles in the ACL Anthology Reference Corpus, published between 1978--2006. In these abstracts, terms (i.e., single or multi-word lexical units with a specialised meaning) are manually annotated. In addition to their boundaries in running text, annotated terms are classified into one of the seven categories method, tool, language resource (LR), LR product, model, measures and measurements, and other. To assess the quality of the annotations and to determine the difficulty of this task, more than 171 of the abstracts are annotated twice, independently, by each of the two annotators. In total, 6,818 terms are identified and annotated, resulting in a specialised vocabulary made of 3,318 lexical forms, mapped to 3,471 concepts.
AMALACH project component TMODS:ENG-CZE; machine translation of queries from Czech to English. This archive contains models for the Moses decoder (binarized, pruned to allow for real-time translation) and configuration files for the MTMonkey toolkit. The aim of this package is to provide a full service for Czech->English translation which can be easily utilized as a component in a larger software solution. (The required tools are freely available and an installation guide is included in the package.)
The translation models were trained on CzEng 1.0 corpus and Europarl. Monolingual data for LM estimation additionally contains WMT news crawls until 2013.
En-De 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->de: 25.9
de->en: 33.4
(Evaluated using multeval: https://github.com/jhclark/multeval)
En-Ru 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->ru: 18.0
ru->en: 30.4
(Evaluated using multeval: https://github.com/jhclark/multeval)
This is the first release of the UFAL Parallel Corpus of North Levantine, compiled by the Institute of Formal and Applied Linguistics (ÚFAL) at Charles University within the Welcome project (https://welcome-h2020.eu/). The corpus consists of 120,600 multiparallel sentences in English, French, German, Greek, Spanish, and Standard Arabic selected from the OpenSubtitles2018 corpus [1] and manually translated into the North Levantine Arabic language. The corpus was created for the purpose of training machine translation for North Levantine and the other languages.