  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
