SANVis: Visual Analytics for Understanding Self-Attention Networks

Cheonbok Park, Jaegul Choo, Inyoup Na, Yongjang Jo, Sungbok Shin, Jaehyo Yoo, Bum Chul Kwon, Jian Zhao, Hyungjong Noh, Yeonsoo Lee

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    21 Citations (Scopus)

    Abstract

    Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications. Recently, they have been further evolved into an advanced approach called multi-head self-attention networks, which can encode a set of input vectors, e.g., word vectors in a sentence, into another set of vectors. Such encoding aims at simultaneously capturing diverse syntactic and semantic features within a set, each of which corresponds to a particular attention head, forming altogether multi-head attention. Meanwhile, the increased model complexity prevents users from easily understanding and manipulating the inner workings of models. To tackle the challenges, we present a visual analytics system called SANVis, which helps users understand the behaviors and the characteristics of multi-head self-attention networks. Using a state-of-the-art self-attention model called Transformer, we demonstrate usage scenarios of SANVis in machine translation tasks. Our system is available at http://short.sanvis.org.

    Original languageEnglish
    Title of host publication2019 IEEE Visualization Conference, VIS 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages146-150
    Number of pages5
    ISBN (Electronic)9781728149417
    DOIs
    Publication statusPublished - 2019 Oct
    Event2019 IEEE Visualization Conference, VIS 2019 - Vancouver, Canada
    Duration: 2019 Oct 202019 Oct 25

    Publication series

    Name2019 IEEE Visualization Conference, VIS 2019

    Conference

    Conference2019 IEEE Visualization Conference, VIS 2019
    Country/TerritoryCanada
    CityVancouver
    Period19/10/2019/10/25

    Bibliographical note

    Publisher Copyright:
    © 2019 IEEE.

    Keywords

    • Deep neural networks
    • interpretability
    • natural language processing
    • self-attention networks
    • visual analytics

    ASJC Scopus subject areas

    • Computer Graphics and Computer-Aided Design
    • Media Technology
    • Modelling and Simulation

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