Sebastien Montella
2021
Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures
Sebastien Montella
|
Lina M. Rojas Barahona
|
Johannes Heinecke
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
2020
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers
Sebastien Montella
|
Betty Fabre
|
Tanguy Urvoy
|
Johannes Heinecke
|
Lina Rojas-Barahona
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
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.
Search