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PENELOPIE: enabling open information extraction for the Greek language through machine translation

Papadopoulos Dimitrios, Papadakis Nikolaos, Matsatsinis Nikolaos

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URIhttp://purl.tuc.gr/dl/dias/0F0D0EB7-47FB-4F26-BD99-416540E4B2FD-
Identifierhttps://doi.org/10.18653/v1/2021.eacl-srw.4-
Identifierhttps://aclanthology.org/2021.eacl-srw.4/-
Languageen-
Extent7 pagesen
TitlePENELOPIE: enabling open information extraction for the Greek language through machine translationen
CreatorPapadopoulos Dimitriosen
CreatorΠαπαδοπουλος Δημητριοςel
CreatorPapadakis Nikolaosen
CreatorMatsatsinis Nikolaosen
CreatorΜατσατσινης Νικολαοςel
PublisherAssociation for Computational Linguisticsen
Content SummaryIn this work, we present a methodology that aims at bridging the gap between high and low-resource languages in the context of Open Information Extraction, showcasing it on the Greek language. The goals of this paper are twofold: First, we build Neural Machine Translation (NMT) models for English-to-Greek and Greek-to-English based on the Transformer architecture. Second, we leverage these NMT models to produce English translations of Greek text as input for our NLP pipeline, to which we apply a series of pre-processing and triple extraction tasks. Finally, we back-translate the extracted triples to Greek. We conduct an evaluation of both our NMT and OIE methods on benchmark datasets and demonstrate that our approach outperforms the current state-of-the-art for the Greek natural language.en
Type of ItemΔημοσίευση σε Συνέδριοel
Type of ItemConference Publicationen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-07-10-
Date of Publication2021-
SubjectNlpen
SubjectMachine-translationen
SubjectInformation-extractionen
SubjectGreek-languageen
SubjectOie-systems en
Bibliographic CitationD. Papadopoulos, N. Papadakis, and N. Matsatsinis, “PENELOPIE: enabling open information extraction for the Greek language through machine translation,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop (EACL 2021), virtual event, 2021, pp. 23–29, doi: 10.18653/v1/2021.eacl-srw.4.en

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