PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation

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.

PDF Abstract EACL 2021 PDF EACL 2021 Abstract


Results from the Paper

 Ranked #1 on Machine Translation on Tatoeba (EN-to-EL) (using extra training data)

     Get a GitHub badge
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Open Information Extraction CaRB OIE benchmark (Greek Use-case) PENELOPIE Greek OIE F1 0.255 # 1
Machine Translation Tatoeba (EL-to-EN) PENELOPIE (Transformers-based Greek-to-English NMT) BLEU 79.3 # 1
Machine Translation Tatoeba (EN-to-EL) PENELOPIE Transformers-based NMT (EN2EL) BLEU 76.9 # 1