PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation
This work addresses the problem of low-resource language processing for Greek in OIE, though it is incremental as it adapts existing methods to a new language.
The paper tackles the lack of Open Information Extraction (OIE) resources for Greek by using Neural Machine Translation (NMT) models to translate Greek text to English, apply OIE, and back-translate results, outperforming the state-of-the-art for Greek.
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.