@inproceedings{jayashree-srijith-2020-evaluation,
title = "Evaluation of Deep {G}aussian Processes for Text Classification",
author = "Jayashree, P. and
Srijith, P. K.",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.185",
pages = "1485--1491",
abstract = "With the tremendous success of deep learning models on computer vision tasks, there are various emerging works on the Natural Language Processing (NLP) task of Text Classification using parametric models. However, it constrains the expressability limit of the function and demands enormous empirical efforts to come up with a robust model architecture. Also, the huge parameters involved in the model causes over-fitting when dealing with small datasets. Deep Gaussian Processes (DGP) offer a Bayesian non-parametric modelling framework with strong function compositionality, and helps in overcoming these limitations. In this paper, we propose DGP models for the task of Text Classification and an empirical comparison of the performance of shallow and Deep Gaussian Process models is made. Extensive experimentation is performed on the benchmark Text Classification datasets such as TREC (Text REtrieval Conference), SST (Stanford Sentiment Treebank), MR (Movie Reviews), R8 (Reuters-8), which demonstrate the effectiveness of DGP models.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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%0 Conference Proceedings
%T Evaluation of Deep Gaussian Processes for Text Classification
%A Jayashree, P.
%A Srijith, P. K.
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F jayashree-srijith-2020-evaluation
%X With the tremendous success of deep learning models on computer vision tasks, there are various emerging works on the Natural Language Processing (NLP) task of Text Classification using parametric models. However, it constrains the expressability limit of the function and demands enormous empirical efforts to come up with a robust model architecture. Also, the huge parameters involved in the model causes over-fitting when dealing with small datasets. Deep Gaussian Processes (DGP) offer a Bayesian non-parametric modelling framework with strong function compositionality, and helps in overcoming these limitations. In this paper, we propose DGP models for the task of Text Classification and an empirical comparison of the performance of shallow and Deep Gaussian Process models is made. Extensive experimentation is performed on the benchmark Text Classification datasets such as TREC (Text REtrieval Conference), SST (Stanford Sentiment Treebank), MR (Movie Reviews), R8 (Reuters-8), which demonstrate the effectiveness of DGP models.
%U https://aclanthology.org/2020.lrec-1.185
%P 1485-1491
Markdown (Informal)
[Evaluation of Deep Gaussian Processes for Text Classification](https://aclanthology.org/2020.lrec-1.185) (Jayashree & Srijith, LREC 2020)
ACL