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

2021-03-28EACL 2021Code Available0· sign in to hype

Dimitris Papadopoulos, Nikolaos Papadakis, Nikolaos Matsatsinis

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Abstract

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.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
Tatoeba (EL-to-EN)PENELOPIE (Transformers-based Greek-to-English NMT)BLEU79.3Unverified
Tatoeba (EN-to-EL)PENELOPIE Transformers-based NMT (EN2EL)BLEU76.9Unverified

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