@inproceedings{blinov-2020-semantic,
title = "Semantic Triples Verbalization with Generative Pre-Training Model",
author = "Blinov, Pavel",
editor = "Castro Ferreira, Thiago and
Gardent, Claire and
Ilinykh, Nikolai and
van der Lee, Chris and
Mille, Simon and
Moussallem, Diego and
Shimorina, Anastasia",
booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
month = "12",
year = "2020",
address = "Dublin, Ireland (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.webnlg-1.17",
pages = "154--158",
abstract = "The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters{'} influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.",
}
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<abstract>The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters’ influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.</abstract>
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%0 Conference Proceedings
%T Semantic Triples Verbalization with Generative Pre-Training Model
%A Blinov, Pavel
%Y Castro Ferreira, Thiago
%Y Gardent, Claire
%Y Ilinykh, Nikolai
%Y van der Lee, Chris
%Y Mille, Simon
%Y Moussallem, Diego
%Y Shimorina, Anastasia
%S Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland (Virtual)
%F blinov-2020-semantic
%X The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters’ influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.
%U https://aclanthology.org/2020.webnlg-1.17
%P 154-158
Markdown (Informal)
[Semantic Triples Verbalization with Generative Pre-Training Model](https://aclanthology.org/2020.webnlg-1.17) (Blinov, WebNLG 2020)
ACL