Dion Wiggins
2022
The EuroPat Corpus: A Parallel Corpus of European Patent Data
Kenneth Heafield
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Elaine Farrow
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Jelmer van der Linde
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Gema Ramírez-Sánchez
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Dion Wiggins
Proceedings of the Thirteenth Language Resources and Evaluation Conference
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.
2020
ParaCrawl: Web-Scale Acquisition of Parallel Corpora
Marta Bañón
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Pinzhen Chen
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Barry Haddow
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Kenneth Heafield
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Hieu Hoang
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Miquel Esplà-Gomis
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Mikel L. Forcada
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Amir Kamran
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Faheem Kirefu
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Philipp Koehn
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Sergio Ortiz Rojas
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Leopoldo Pla Sempere
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Gema Ramírez-Sánchez
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Elsa Sarrías
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Marek Strelec
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Brian Thompson
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William Waites
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Dion Wiggins
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Jaume Zaragoza
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their quality and their usefulness to create machine translation systems.
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Co-authors
- Kenneth Heafield 2
- Gema Ramírez‐Sánchez 2
- Elaine Farrow 1
- Jelmer Van Der Linde 1
- Marta Bañón 1
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