In this paper we present our submission for the EACL 2021 SRW; 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.
@article{arxiv.2103.15075,
title = {PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation},
author = {Dimitris Papadopoulos and Nikolaos Papadakis and Nikolaos Matsatsinis},
journal= {arXiv preprint arXiv:2103.15075},
year = {2021}
}
Comments
16th conference of the European Chapter of the Association for Computational Linguistics Student Research Workshop (EACL 2021 SRW)