ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts

Felipe Soares, Mark Stevenson, Diego Bartolome, Anna Zaretskaya

Abstract
The Google Patents is one of the main important sources of patents information. A striking characteristic is that many of its abstracts are presented in more than one language, thus making it a potential source of parallel corpora. This article presents the development of a 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. We demonstrate the capabilities of our corpus by training Neural Machine Translation (NMT) models for the main 9 language pairs, with a total of 18 models. Our parallel corpus is freely available in TSV format and with a SQLite database, with complementary information regarding patent metadata.
Anthology ID:
2020.lrec-1.465
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3769–3774
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.465
DOI:
Bibkey:
Cite (ACL):
Felipe Soares, Mark Stevenson, Diego Bartolome, and Anna Zaretskaya. 2020. ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 3769–3774, Marseille, France. European Language Resources Association.
Cite (Informal):
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts (Soares et al., LREC 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/landing_page/2020.lrec-1.465.pdf
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