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        <title>Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension</title>
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        <namePart type="given">Shusen</namePart>
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    <name type="personal">
        <namePart type="given">Vivek</namePart>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
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    <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 the relationships among the input, the model internals (i.e., attention), and the output predictions, which in turn shed light on the model decision-making process.</abstract>
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%0 Conference Proceedings
%T Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension
%A Liu, Shusen
%A Li, Tao
%A Li, Zhimin
%A Srikumar, Vivek
%A Pascucci, Valerio
%A Bremer, Peer-Timo
%Y Blanco, Eduardo
%Y Lu, Wei
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F liu-etal-2018-visual
%X 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 the relationships among the input, the model internals (i.e., attention), and the output predictions, which in turn shed light on the model decision-making process.
%R 10.18653/v1/D18-2007
%U https://aclanthology.org/D18-2007
%U https://doi.org/10.18653/v1/D18-2007
%P 36-41

Markdown (Informal)

[Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension](https://aclanthology.org/D18-2007) (Liu et al., EMNLP 2018)

• Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension (Liu et al., EMNLP 2018)

ACL

• Shusen Liu, Tao Li, Zhimin Li, Vivek Srikumar, Valerio Pascucci, and Peer-Timo Bremer. 2018. Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 36–41, Brussels, Belgium. Association for Computational Linguistics.