  Frame shift is a cross-linguistic phenomenon in translation which results in
corresponding pairs of linguistic material evoking different frames. The
ability to predict frame shifts enables automatic creation of multilingual
FrameNets through annotation projection. Here, we propose the Frame Shift
Prediction task and demonstrate that graph attention networks, combined with
auxiliary training, can learn cross-linguistic frame-to-frame correspondence
and predict frame shifts.
