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arXiv · 2012.00571

Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers

Sebastien Montella·Betty Fabre·Tanguy Urvoy·Johannes Heinecke·Lina Rojas-Barahona
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Abstract

The task of verbalization of RDF triples has known a growth in popularity due to the rising ubiquity of Knowledge Bases (KBs). The formalism of RDF triples is a simple and efficient way to store facts at a large scale. However, its abstract representation makes it difficult for humans to interpret. For this purpose, the WebNLG challenge aims at promoting automated RDF-to-text generation. We propose to leverage pre-trainings from augmented data with the Transformer model using a data augmentation strategy. Our experiment results show a minimum relative increases of 3.73%, 126.05% and 88.16% in BLEU score for seen categories, unseen entities and unseen categories respectively over the standard training.

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Sebastien Montella, Betty Fabre, Tanguy Urvoy, Johannes Heinecke, Lina Rojas-Barahona. 2020-12-01. Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers. https://arxiv.org/abs/2012.00571

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