Impact of Training Dataset Size on Neural Answer Selection Models

It is held as a truism that deep neural networks require large datasets to train effective models. However, large datasets, especially with high-quality labels, can be expensive to obtain. This study sets out to investigate (i) how large a dataset must be to train well-performing models, and (ii) wh...

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Bibliographic Details
Published in:arXiv.org 2019-01
Main Authors: Linjordet, Trond, Balog, Krisztian
Format: Article
Language:English
Subjects:
Datasets
Neural networks
Training
Online Access:Get full text
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Summary:It is held as a truism that deep neural networks require large datasets to train effective models. However, large datasets, especially with high-quality labels, can be expensive to obtain. This study sets out to investigate (i) how large a dataset must be to train well-performing models, and (ii) what impact can be shown from fractional changes to the dataset size. A practical method to investigate these questions is to train a collection of deep neural answer selection models using fractional subsets of varying sizes of an initial dataset. We observe that dataset size has a conspicuous lack of effect on the training of some of these models, bringing the underlying algorithms into question.
ISSN:2331-8422