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

12/01/2020
by   Sebastien Montella, et al.
0

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 for seen categories, unseen entities and unseen categories respectively over the standard training.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/12/2020

Data augmentation enhanced speaker enrollment for text-dependent speaker verification

Data augmentation is commonly used for generating additional data from t...
09/13/2019

Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting

We study sequence-to-sequence (seq2seq) pre-training with data augmentat...
06/16/2022

MixGen: A New Multi-Modal Data Augmentation

Data augmentation is a necessity to enhance data efficiency in deep lear...
07/14/2020

Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR

Recently Deep Transformer models have proven to be particularly powerful...
10/11/2020

PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation

De-identification is the task of identifying protected health informatio...