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The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Ralf Steinberger, Bruno Pouliquen, Anna Widiger, Camelia Ignat, Tomaz Erjavec, Dan Tufis, Daniel Varga

Computation and Language · Submitted 12 Sep 2006

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Due to the large number of parallel texts in many languages, the JRC-Acquis is particularly suitable to carry out all types of cross-language research, as well as to test and benchmark text analysis software across different languages (for instance for alignment, sentence splitting and term extraction).

how it is stated · asserted 0.88 read the record →

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arXiv IDcs/0609058 (version 1)
Pith IDN3QFFXZ4, Pith's permanent record of this paper and its review
CitationAnna Widiger, Bruno Pouliquen, Camelia Ignat, Daniel Varga, Dan Tufis, Ralf Steinberger, Tomaz Erjavec. "The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages." arXiv:cs/0609058 (2006). https://pith.science/paper/N3QFFXZ4
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,609 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.32 · states limitation 0.08 · human participants 0.10 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.96 · compares with prior results 0.06 · mentions code or data availability 0.94.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present a new, unique and freely available parallel corpus containing European Union (EU) documents of mostly legal nature. It is available in all 20 official EUanguages, with additional documents being available in the languages of the EU candidate countries. The corpus consists of almost 8,000 documents per language, with an average size of nearly 9 million words per language. Pair-wise paragraph alignment information produced by two different aligners (Vanilla and HunAlign) is available for all 190+ language pair combinations. Most texts have been manually classified according to the EUROVOC subject domains so that the collection can also be used to train and test multi-label classification algorithms and keyword-assignment software. The corpus is encoded in XML, according to the Text Encoding Initiative Guidelines. Due to the large number of parallel texts in many languages, the JRC-Acquis is particularly suitable to carry out all types of cross-language research, as well as to test and benchmark text analysis software across different languages (for instance for alignment, sentence splitting and term extraction).

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present a new, unique and freely available parallel corpus containing European Union (EU) documents of mostly legal nature. contribution 1.00 0.89 0.35 asserted 1.00
s1 It is available in all 20 official EUanguages, with additional documents being available in the languages of the EU candidate countries. contribution 0.90 0.54 0.06 asserted 1.00
s2 The corpus consists of almost 8,000 documents per language, with an average size of nearly 9 million words per language. contribution 0.62 0.83 0.06 asserted 1.00
s3 Pair-wise paragraph alignment information produced by two different aligners (Vanilla and HunAlign) is available for all 190+ language pair combinations. contribution 0.69 0.79 0.07 asserted 1.00
s4 Most texts have been manually classified according to the EUROVOC subject domains so that the collection can also be used to train and test multi-label classification algorithms and keyword-assignment software. contribution 0.54 0.84 0.08 asserted 0.99
s5 The corpus is encoded in XML, according to the Text Encoding Initiative Guidelines. method 0.83 0.73 0.04 asserted 1.00
s6 Due to the large number of parallel texts in many languages, the JRC-Acquis is particularly suitable to carry out all types of cross-language research, as well as to test and benchmark text analysis software across different languages (for instance for alignment, sentence splitting and term extraction). interpretation 0.96 0.29 0.44 asserted 0.88

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.50.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present a new, unique and freely available parallel corpus containing European Union (EU) documents of mostly legal nature. It is available in all 20 official EUanguages, with additional documents being available in the languages of the EU candidate countries. The corpus consists of almost 8,000 documents per language, with an average size of nearly 9 million words per language. Pair-wise paragraph alignment information produced by two different aligners (Vanilla and HunAlign) is available for all 190+ language pair combinations. Most texts have been manually classified according to the EUROVOC subject domains so that the collection can also be used to train and test multi-label classification algorithms and keyword-assignment software. The corpus is encoded in XML, according to the Text Encoding Initiative Guidelines. Due to the large number of parallel texts in many languages, the JRC-Acquis is particularly suitable to carry out all types of cross-language research, as well as to test and benchmark text analysis software across different languages (for instance for alignment, sentence splitting and term extraction).

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References
How the literature uses it

3 arXiv papers cite this in 3 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 1 sentence without a confident label

Uses its data or values 1 paper
Cites it as background 1 paper
  • Many other highly multilingual corpora have, of course, also become available in the many years since MULTEXT, with the best known being Europarl [ref] , JRC-ACQUIS [ref] and other corpora compiled by JRC.

    MULTEXT-East · Parallel annotated corpora · 0.94

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