PENELOPIE: Enabling open information extraction for the Greek language through machine translation
D Papadopoulos, N Papadakis… - arXiv preprint arXiv …, 2021 - arxiv.org
arXiv preprint arXiv:2103.15075, 2021•arxiv.org
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 …
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 …
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.
arxiv.org