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We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.
This is a multilingual parallel corpus created from translations of the Bible compiled by Christos Christodoulopoulos and Mark Steedman.
102 languages, 5,148 bitexts
total number of files: 107
total number of tokens: 56.43M
total number of sentence fragments: 2.84M
This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. No claims of intellectual property are made on the work of preparation of the corpus.
This dataset is designed to provide training data
for common sense relationships pulls together from various sources.
The dataset is multi-lingual. See langauge codes and language info
here: https://github.com/commonsense/conceptnet5/wiki/Languages
This dataset provides an interface for the conceptnet5 csv file, and
some (but not all) of the raw text data used to build conceptnet5:
omcsnet_sentences_free.txt, and omcsnet_sentences_more.txt.
One use of this dataset would be to learn to extract the conceptnet
relationship from the omcsnet sentences.
Conceptnet5 has 34,074,917 relationships. Of those relationships,
there are 2,176,099 surface text sentences related to those 2M
entries.
omcsnet_sentences_free has 898,161 lines. omcsnet_sentences_more has
2,001,736 lines.
Original downloads are available here
https://github.com/commonsense/conceptnet5/wiki/Downloads. For more
information, see: https://github.com/commonsense/conceptnet5/wiki
The omcsnet data comes with the following warning from the authors of
the above site: Remember: this data comes from various forms of
crowdsourcing. Sentences in these files are not necessarily true,
useful, or appropriate.
Original source: Website and documentatuion from the European Central Bank, compiled and made available by Alberto Simoes (thank you very much!)
19 languages, 170 bitexts
total number of files: 340
total number of tokens: 757.37M
total number of sentence fragments: 30.55M
This is a parallel corpus made out of PDF documents from the European Medicines Agency. All files are automatically converted from PDF to plain text using pdftotext with the command line arguments -layout -nopgbrk -eol unix. There are some known problems with tables and multi-column layouts - some of them are fixed in the current version.
source: http://www.emea.europa.eu/
22 languages, 231 bitexts
total number of files: 41,957
total number of tokens: 311.65M
total number of sentence fragments: 26.51M
WebNLG is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the development and evaluation of popular tasks in the NLG pipeline architecture (Reiter and Dale, 2000), such as Discourse Ordering, Lexicalization, Aggregation and Referring Expression Generation.
The corpora comprise of files per data provider that are encoded in the IOB format (Ramshaw & Marcus, 1995). The IOB format is a simple text chunking format that divides texts into single tokens per line, and, separated by a whitespace, tags to mark named entities. The most commonly used categories for tags are PER (person), LOC (location) and ORG (organization). To mark named entities that span multiple tokens, the tags have a prefix of either B- (beginning of named entity) or I- (inside of named entity). O (outside of named entity) tags are used to mark tokens that are not a named entity.
In October 2012, the European Union's (EU) Directorate General for Education and Culture ( DG EAC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-six languages. This resource bears the name EAC Translation Memory, short EAC-TM.
EAC-TM covers up to 26 languages: 22 official languages of the EU (all except Irish) plus Icelandic, Croatian, Norwegian and Turkish. EAC-TM thus contains translations from English into the following 25 languages: Bulgarian, Czech, Danish, Dutch, Estonian, German, Greek, Finnish, French, Croatian, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish and Turkish.
All documents and sentences were originally written in English (source language is English) and then translated into the other languages. The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language.
In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages. This resource bears the name EAC Translation Memory, short EAC-TM.
ECDC-TM covers 25 languages: the 23 official languages of the EU plus Norwegian (Norsk) and Icelandic. ECDC-TM was created by translating from English into the following 24 languages: Bulgarian, Czech, Danish, Dutch, English, Estonian, Gaelige (Irish), German, Greek, Finnish, French, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian (NOrsk), Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish and Swedish.
All documents and sentences were thus originally written in English. They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg.
EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations.
It consists of more than 24,000 high-quality high school exam questions in 16 languages,
covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.
GermaNER is a freely available statistical German Named Entity Tagger based on conditional random fields(CRF). The tagger is trained and evaluated on the NoSta-D Named Entity dataset, which was used in the GermEval 2014 for named entity recognition. The tagger comes close to the performance of the best (proprietary) system in the competition with 77% F-measure (this is the latest result; the one reported in the paper is 76%) test set performance on the four standard NER classes (PERson, LOCation, ORGanisation and OTHer).
We describe a range of features and their influence on German NER classification and provide a comparative evaluation and some analysis of the results. The software components, the training data and all data used for feature generation are distributed under permissive licenses, thus this tagger can be used in academic and commercial settings without restrictions or fees. The tagger is available as a command-line tool and as an Apache UIMA component.
This dataset is intended to advance topic classification for German texts. A classifier that is efffective in
English may not be effective in German dataset because it has a higher inflection and longer compound words.
The 10kGNAD dataset contains 10273 German news articles from an Austrian online newspaper categorized into
9 categories. Article titles and text are concatenated together and authors are removed to avoid a keyword-like
classification on authors that write frequently about one category. This dataset can be used as a benchmark
for German topic classification.
A parallel corpus of KDE4 localization files (v.2).
92 languages, 4,099 bitexts
total number of files: 75,535
total number of tokens: 60.75M
total number of sentence fragments: 8.89M
We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs sampled from the Google Natural Questions dataset, aligned across 26 typologically diverse languages (260k question-answer pairs in total). For each query we collected new passage-independent answers. These queries and answers were then human translated into 25 Non-English languages.
The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products.
It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary
for language development in the nearly 100 languages available. Terminology is provided in .tbx format, an industry standard for terminology exchange.
Parallel corpora from Web Crawls collected in the ParaCrawl project and further processed for making it a multi-parallel corpus by pivoting via English. Here we only provide the additional language pairs that came out of pivoting. The bitexts for English are available from the ParaCrawl release.
40 languages, 669 bitexts
total number of files: 40
total number of tokens: 10.14G
total number of sentence fragments: 505.48M
Please, acknowledge the ParaCrawl project at http://paracrawl.eu. This version is derived from the original release at their website adjusted for redistribution via the OPUS corpus collection. Please, acknowledge OPUS as well for this service.
A parallel corpus of News Commentaries provided by WMT for training SMT. The source is taken from CASMACAT: http://www.casmacat.eu/corpus/news-commentary.html
12 languages, 63 bitexts
total number of files: 61,928
total number of tokens: 49.66M
total number of sentence fragments: 1.93M
The “One Million Posts” corpus is an annotated data set consisting of
user comments posted to an Austrian newspaper website (in German language).
DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website,
there is a discussion section below each news article where readers engage in
online discussions. The data set contains a selection of user posts from the
12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and
1,000,000 unlabeled posts in the data set. The labeled posts were annotated by
professional forum moderators employed by the newspaper.
The data set contains the following data for each post:
* Post ID
* Article ID
* Headline (max. 250 characters)
* Main Body (max. 750 characters)
* User ID (the user names used by the website have been re-mapped to new numeric IDs)
* Time stamp
* Parent post (replies give rise to tree-like discussion thread structures)
* Status (online or deleted by a moderator)
* Number of positive votes by other community members
* Number of negative votes by other community members
For each article, the data set contains the following data:
* Article ID
* Publishing date
* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)
* Title
* Body
Detailed descriptions of the post selection and annotation procedures are given in the paper.
## Annotated Categories
Potentially undesirable content:
* Sentiment (negative/neutral/positive)
An important goal is to detect changes in the prevalent sentiment in a discussion, e.g.,
the location within the fora and the point in time where a turn from positive/neutral
sentiment to negative sentiment takes place.
* Off-Topic (yes/no)
Posts which digress too far from the topic of the corresponding article.
* Inappropriate (yes/no)
Swearwords, suggestive and obscene language, insults, threats etc.
* Discriminating (yes/no)
Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.
Neutral content that requires a reaction:
* Feedback (yes/no)
Sometimes users ask questions or give feedback to the author of the article or the
newspaper in general, which may require a reply/reaction.
Potentially desirable content:
* Personal Stories (yes/no)
In certain fora, users are encouraged to share their personal stories, experiences,
anecdotes etc. regarding the respective topic.
* Arguments Used (yes/no)
It is desirable for users to back their statements with rational argumentation,
reasoning and sources.
This is a new collection of translated movie subtitles from http://www.opensubtitles.org/.
IMPORTANT: If you use the OpenSubtitle corpus: Please, add a link to http://www.opensubtitles.org/ to your website and to your reports and publications produced with the data!
This is a slightly cleaner version of the subtitle collection using improved sentence alignment and better language checking.
62 languages, 1,782 bitexts
total number of files: 3,735,070
total number of tokens: 22.10G
total number of sentence fragments: 3.35G
OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side.
The corpus covers 100 languages (including English).OPUS-100 contains approximately 55M sentence pairs.
Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k.
This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php
Note that the texts are rather dated due to copyright issues and that some of them are manually reviewed (check the meta-data at the top of the corpus files in XML). The source is multilingually aligned, which is available from http://www.farkastranslations.com/bilingual_books.php. In OPUS, the alignment is formally bilingual but the multilingual alignment can be recovered from the XCES sentence alignment files. Note also that the alignment units from the original source may include multi-sentence paragraphs, which are split and sentence-aligned in OPUS.
All texts are freely available for personal, educational and research use. Commercial use (e.g. reselling as parallel books) and mass redistribution without explicit permission are not granted. Please acknowledge the source when using the data!
16 languages, 64 bitexts
total number of files: 158
total number of tokens: 19.50M
total number of sentence fragments: 0.91M
A collection of translation memories provided by the JRC. Source: https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory
25 languages, 299 bitexts
total number of files: 817,410
total number of tokens: 2.13G
total number of sentence fragments: 113.52M
A parallel corpus collected from the European Constitution for 21 language.
A parallel corpus of GNOME localization files. Source: https://l10n.gnome.org
187 languages, 12,822 bitexts
total number of files: 113,344
total number of tokens: 267.27M
total number of sentence fragments: 58.12M
A collection of documents from http://www.openoffice.org/.
Parallel corpora from Web Crawls collected in the ParaCrawl project
40 languages, 41 bitexts
total number of files: 20,995
total number of tokens: 21.40G
total number of sentence fragments: 1.12G
RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations.
A parallel corpus of Ubuntu localization files. Source: https://translations.launchpad.net
244 languages, 23,988 bitexts
total number of files: 30,959
total number of tokens: 29.84M
total number of sentence fragments: 7.73M
This is a corpus of parallel sentences extracted from Wikipedia by Krzysztof Wołk and Krzysztof Marasek. Please cite the following publication if you use the data: Krzysztof Wołk and Krzysztof Marasek: Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs., Procedia Technology, 18, Elsevier, p.126-132, 2014
20 languages, 36 bitexts
total number of files: 114
total number of tokens: 610.13M
total number of sentence fragments: 25.90M
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.
PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages.
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine
translated training pairs in six typologically distinct languages: French, Spanish, German,
Chinese, Japanese, and Korean. English language is available by default. All translated
pairs are sourced from examples in PAWS-Wiki.
For further details, see the accompanying paper: PAWS-X: A Cross-lingual Adversarial Dataset
for Paraphrase Identification (https://arxiv.org/abs/1908.11828)
NOTE: There might be some missing or wrong labels in the dataset and we have replaced them with -1.
A parallel corpus originally extracted from http://se.php.net/download-docs.php. The original documents are written in English and have been partly translated into 21 languages. The original manuals contain about 500,000 words. The amount of actually translated texts varies for different languages between 50,000 and 380,000 words. The corpus is rather noisy and may include parts from the English original in some of the translations. The corpus is tokenized and each language pair has been sentence aligned.
23 languages, 252 bitexts
total number of files: 71,414
total number of tokens: 3.28M
total number of sentence fragments: 1.38M
The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform.
Developed by: Qatar Computing Research Institute, Arabic Language Technologies Group
The QED Corpus is made public for RESEARCH purpose only.
The corpus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Copyright Qatar Computing Research Institute. All rights reserved.
225 languages, 9,291 bitexts
total number of files: 271,558
total number of tokens: 371.76M
total number of sentence fragments: 30.93M
This dataset add sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them.
SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, and pos-tagging. The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1].
DFKI SmartData Corpus is a dataset of 2598 German-language documents
which has been annotated with fine-grained geo-entities, such as streets,
stops and routes, as well as standard named entity types. It has also
been annotated with a set of 15 traffic- and industry-related n-ary
relations and events, such as Accidents, Traffic jams, Acquisitions,
and Strikes. The corpus consists of newswire texts, Twitter messages,
and traffic reports from radio stations, police and railway companies.
It allows for training and evaluating both named entity recognition
algorithms that aim for fine-grained typing of geo-entities, as well
as n-ary relation extraction systems.
This is a collection of Quran translations compiled by the Tanzil project
The translations provided at this page are for non-commercial purposes only. If used otherwise, you need to obtain necessary permission from the translator or the publisher.
If you are using more than three of the following translations in a website or application, we require you to put a link back to this page to make sure that subsequent users have access to the latest updates.
42 languages, 878 bitexts
total number of files: 105
total number of tokens: 22.33M
total number of sentence fragments: 1.01M
A freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences and translations for particular linguistic constructions and words. The paraphrase corpus is created by populating a graph with Tatoeba sentences and equivalence links between sentences “meaning the same thing”. This graph is then traversed to extract sets of paraphrases. Several language-independent filters and pruning steps are applied to remove uninteresting sentences. A manual evaluation performed on three languages shows that between half and three quarters of inferred paraphrases are correct and that most remaining ones are either correct but trivial, or near-paraphrases that neutralize a morphological distinction. The corpus contains a total of 1.9 million sentences, with 200 – 250 000 sentences per language. It covers a range of languages for which, to our knowledge,no other paraphrase dataset exists.
This is a collection of translated sentences from Tatoeba
359 languages, 3,403 bitexts
total number of files: 750
total number of tokens: 65.54M
total number of sentence fragments: 8.96M
A parallel corpus of TED talk subtitles provided by CASMACAT: http://www.casmacat.eu/corpus/ted2013.html. The files are originally provided by https://wit3.fbk.eu.
15 languages, 14 bitexts
total number of files: 28
total number of tokens: 67.67M
total number of sentence fragments: 3.81M
The core of WIT3 is the TED Talks corpus, that basically redistributes the original content published by the TED Conference website (http://www.ted.com). Since 2007,
the TED Conference, based in California, has been posting all video recordings of its talks together with subtitles in English
and their translations in more than 80 languages. Aside from its cultural and social relevance, this content, which is published under the Creative Commons BYNC-ND license, also represents a precious
language resource for the machine translation research community, thanks to its size, variety of topics, and covered languages.
This effort repurposes the original content in a way which is more convenient for machine translation researchers.
This is the Tilde MODEL Corpus – Multilingual Open Data for European Languages.
The data has been collected from sites allowing free use and reuse of its content, as well as from Public Sector web sites. The activities have been undertaken as part of the ODINE Open Data Incubator for Europe, which aims to support the next generation of digital businesses and fast-track the development of new products and services. The corpus includes the following parts:
Tilde MODEL - EESC is a multilingual corpus compiled from document texts of European Economic and Social Committee document portal. Source: http://dm.eesc.europa.eu/
Tilde MODEL - RAPID multilingual parallel corpus is compiled from all press releases of Press Release Database of European Commission released between 1975 and end of 2016 as available from http://europa.eu/rapid/
Tilde MODEL - ECB multilingual parallel corpus is compiled from the multilingual pages of European Central Bank web site http://ebc.europa.eu/
Tilde MODEL - EMA is a corpus compiled from texts of European Medicines Agency document portal as available in http://www.ema.europa.eu/ at the end of 2016
Tilde MODEL - World Bank is a corpus compiled from texts of World Bank as available in http://www.worldbank.org/ in 2017
Tilde MODEL - AirBaltic.com Travel Destinations is a multilingual parallel corpus compiled from description texts of AirBaltic.com travel destinations as available in https://www.airbaltic.com/en/destinations/ in 2017
Tilde MODEL - LiveRiga.com is a multilingual parallel corpus compiled from Riga tourist attractions description texts of http://liveriga.com/ web site in 2017
Tilde MODEL - Lithuanian National Philharmonic Society is a parallel corpus compiled from texts of Lithuanian National Philharmonic Society web site http://www.filharmonija.lt/ in 2017
Tilde MODEL - mupa.hu is a parallel corpus from texts of Müpa Budapest - web site of Hungarian national culture house and concert venue https://www.mupa.hu/en/ compiled in spring of 2017
Tilde MODEL - fold.lv is a parallel corpus from texts of fold.lv portal http://www.fold.lv/en/ of the best of Latvian and foreign creative industries as compiled in spring of 2017
Tilde MODEL - czechtourism.com is a multilingual parallel corpus from texts of http://czechtourism.com/ portal compiled in spring of 2017
30 languages, 274 bitexts
total number of files: 125
total number of tokens: 1.43G
total number of sentence fragments: 62.44M
The Universal Declaration of Human Rights (UDHR) is a milestone document in the history of human rights. Drafted by
representatives with different legal and cultural backgrounds from all regions of the world, it set out, for the
first time, fundamental human rights to be universally protected. The Declaration was adopted by the UN General
Assembly in Paris on 10 December 1948 during its 183rd plenary meeting. The dataset includes translations of the
document in 464 languages and dialects.
© 1996 – 2009 The Office of the High Commissioner for Human Rights
This plain text version prepared by the “UDHR in Unicode” project, https://www.unicode.org/udhr.
This is a collection of translated documents from the United Nations. This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
A dataset of atomic wikipedia edits containing insertions and deletions of a contiguous chunk of text in a sentence. This dataset contains ~43 million edits across 8 languages.
An atomic edit is defined as an edit e applied to a natural language expression S as the insertion, deletion, or substitution of a sub-expression P such that both the original expression S and the resulting expression e(S) are well-formed semantic constituents (MacCartney, 2009). In this corpus, we release such atomic insertions and deletions made to sentences in wikipedia.
WikiLingua is a large-scale multilingual dataset for the evaluation of
crosslingual abstractive summarization systems. The dataset includes ~770k
article and summary pairs in 18 languages from WikiHow. The gold-standard
article-summary alignments across languages was done by aligning the images
that are used to describe each how-to step in an article.
WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus.
It is a benchmark dataset for language identification and contains 235000 paragraphs of 235 languages
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource
English--German (En-De) and English--Chinese (En-Zh), medium-resource
Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource
Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a
dataset with a combination of Wikipedia articles and Reddit articles
for Russian-English (En-Ru). The datasets were collected by translating
sentences sampled from source language articles using state-of-the-art NMT
models built using the fairseq toolkit and annotated with Direct Assessment (DA)
scores by professional translators. Each sentence was annotated following the
FLORES setup, which presents a form of DA, where at least three professional
translators rate each sentence from 0-100 according to the perceived translation
quality. DA scores are standardised using the z-score by rater. Participating systems
are required to score sentences according to z-standardised DA scores.
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 2 evaluates the application of QE for post-editing purposes. It consists of predicting:
- A/ Word-level tags. This is done both on source side (to detect which words caused errors)
and target side (to detect mistranslated or missing words).
- A1/ Each token is tagged as either `OK` or `BAD`. Additionally,
each gap between two words is tagged as `BAD` if one or more
missing words should have been there, and `OK` otherwise. Note
that number of tags for each target sentence is 2*N+1, where
N is the number of tokens in the sentence.
- A2/ Tokens are tagged as `OK` if they were correctly
translated, and `BAD` otherwise. Gaps are not tagged.
- B/ Sentence-level HTER scores. HTER (Human Translation Error Rate)
is the ratio between the number of edits (insertions/deletions/replacements)
needed and the reference translation length.
Wizard-of-Oz (WOZ) is a dataset for training task-oriented dialogue systems. The dataset is designed around the task of finding a restaurant in the Cambridge, UK area. There are three informable slots (food, pricerange,area) that users can use to constrain the search and six requestable slots (address, phone, postcode plus the three informable slots) that the user can ask a value for once a restaurant has been offered.
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)
For more information, please take a look at https://microsoft.github.io/XGLUE/.
XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.