Original paper
Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension
Published: Jan 1, 2018
Abstract
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. In this work, we present a flexible visualization library for creating customized visual analytic environments, in which the user can investigate and interrogate...
Paper Details
Title
Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension
Published Date
Jan 1, 2018
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