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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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Year 2021
Type of Item Conference Publication
Bibliographic Citation D. 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.
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In 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.

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