The EuroPat Corpus: A Parallel Corpus of European Patent Data

Kenneth Heafield, Elaine Farrow, Jelmer Van Der Linde, Gema Ramírez-Sánchez, Dion Wiggins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present the EuroPat corpus of patent-specific parallel data for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. The filtered parallel corpora range in size from 51 million sentences (Spanish-English) to 154k sentences (Croatian-English), with the unfiltered (raw) corpora being up to 2 times larger. Access to clean, high quality, parallel data in technical domains such as science, engineering, and medicine is needed for training neural machine translation systems for tasks like online dispute resolution and eProcurement. Our evaluation found that the addition of EuroPat data to a generic baseline improved the performance of machine translation systems on in-domain test data in German, Spanish, French, and Polish; and in translating patent data from Croatian to English. The corpus has been released under Creative Commons Zero, and is expected to be widely useful for training high-quality machine translation systems, and particularly for those targeting technical documents such as patents and contracts.
Original languageEnglish
Title of host publicationProceedings of the 13th Language Resources and Evaluation Conference
Number of pages9
Publication statusAccepted/In press - 4 Apr 2022
Event13th Conference on Language Resources and Evaluation - Marseille, France
Duration: 20 Jun 202225 Jun 2022
Conference number: 13
https://lrec2022.lrec-conf.org/en/

Conference

Conference13th Conference on Language Resources and Evaluation
Abbreviated titleLREC 2022
Country/TerritoryFrance
CityMarseille
Period20/06/2225/06/22
Internet address

Keywords

  • Parallel data
  • Corpus
  • Patent
  • Legal
  • Technical translation

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