Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension

EMNLP 2018 Shusen LiuTao LiZhimin LiVivek SrikumarValerio PascucciPeer-Timo Bremer

Neural networks models have gained unprecedented popularity in natural language processing due to their state-of-the-art performance and the flexible end-to-end training scheme. Despite their advantages, the lack of interpretability hinders the deployment and refinement of the models... (read more)

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