NLIZE : A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models
With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-syst...
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Détails bibliographiques
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1996. - (2018) vom: 05. Sept.
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| Auteur principal: |
Liu, Shusen
(Auteur) |
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| Autres auteurs: |
Li, Zhimin,
Li, Tao,
Srikumar, Vivek,
Pascucci, Valerio,
Bremer, Peer-Timo |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2018
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| Accès à la collection: | IEEE transactions on visualization and computer graphics
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| Sujets: | Journal Article |
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