Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers
By Sébastien Montella, Betty Fabre, Tanguy Urvoy, Johannes Heinecke, Lina Maria Rojas-Barahona. 2020
15 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Varun Prashant Gangal
- Soroush Vosoughi
- Eduard H. Hovy
- Wanxiang Che
- Teruko Mitamura
- Jason Wei
- Yutai Hou
- Steven Y. Feng
In Natural Language Processing
- Varun Prashant Gangal
- Eduard H. Hovy
- Teruko Mitamura
- Soroush Vosoughi
- Wanxiang Che
- Jason Wei
- Yutai Hou
- Steven Y. Feng
Citations in the corpus
- A Survey of Data Augmentation Approaches for NLP (2021, methodology): “Montella et al. (2020) show performance gains on WebNLG by DA using Wikipedia sentences as targets and parsed OpenIE triples as inputs.”
- Data Augmentation Approaches in Natural Language Processing: A Survey (2021, methodology): “[34] make use of Wikipedia to leverage a massive sentences.”
- The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020) (2020, unknown): “(2020) explore data augmentation for RDF-to-English generation.”