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

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arxiv cs/0609058 v1 pith:N3QFFXZ4 submitted 2006-09-12 cs.CL

Ralf Steinberger , Bruno Pouliquen , Anna Widiger , Camelia Ignat , Tomaz Erjavec , Dan Tufis , Daniel Varga This is my paper · ORCID
classification cs.CL
keywords availablecorpuslanguagesdocumentslanguageparallelaccordingalignment
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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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Cited by 2 Pith papers

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    Treating lexical constraints as soft external memories lets an NMT model correct noisy user hints and benefit from automatically generated hints, improving BLEU over hard constrained decoding.

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    A survey of RAG paradigms, components, benchmarks, and challenges for improving LLMs on knowledge-intensive tasks.

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