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BY 4.0 license Open Access Published by De Gruyter Open Access July 30, 2019

Unsupervised and weakly supervised approaches for answer selection tasks with scarce annotations

  • Emmanuel Vallee EMAIL logo , Delphine Charlet , Francesca Galassi , Gabriel Marzinotto , Fabrice Clérot and Frank Meyer
From the journal Open Computer Science


Addressing Answer Selection (AS) tasks with complex neural networks typically requires a large amount of annotated data to increase the accuracy of the models. In this work, we are interested in simple models that can potentially give good performance on datasets with no or few annotations. First, we propose new unsupervised baselines that leverage distributed word and sentence representations. Second, we compare the ability of our neural architectures to learn from few annotated examples in a weakly supervised scheme and we demonstrate how these methods can benefit from a pre-training on an external dataset. With an emphasis on results reproducibility, we show that our simple methods can reach or approach state-of-the-art performances on four common AS datasets.


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Received: 2019-02-20
Accepted: 2019-06-25
Published Online: 2019-07-30

© 2019 Emmanuel Vallee et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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