{"content":[{"text":"Attention and Latent alignment","bbox":{"left":0.023438,"top":0.033759,"width":0.5108860000000001,"height":0.053193000000000004}},{"text":"attention decisions directly as a tool for model interpretability","bbox":{"left":0.090198,"top":0.3119,"width":0.776432,"height":0.04044900000000001}},{"text":"or as a factor in ﬁnal predictions","bbox":{"left":0.090198,"top":0.372673,"width":0.40797000000000005,"height":0.04044900000000001}},{"text":"attention plays the role of a latent alignment variable","bbox":{"left":0.090198,"top":0.433446,"width":0.670659,"height":0.04044900000000001}},{"text":"hard attention: explicit by introducing a latent variable for alignment \n and then optimizing a bound on the log marginal likelihood using \n policy gradients","bbox":{"left":0.090198,"top":0.49422,"width":0.868495,"height":0.14003100000000002}},{"text":"VQA and NMT: alignment","bbox":{"left":0.090198,"top":0.654576,"width":0.336472,"height":0.04044899999999996}},{"text":"10Latent Alignment and V \n Reviewed by : Arshdeep Sekhon (University of Summer Virginia) Review 2 / 19","bbox":{"left":0.0,"top":0.932681,"width":1.0188270000000001,"height":0.07653699999999997}}],"images_bbox":[{"left":0.060634,"top":0.319705,"width":0.016536,"height":0.022049},{"left":0.060634,"top":0.382176,"width":0.016536,"height":0.022049},{"left":0.060634,"top":0.440972,"width":0.016536,"height":0.025723},{"left":0.060634,"top":0.503443,"width":0.016536,"height":0.022049},{"left":0.060634,"top":0.665133,"width":0.016536,"height":0.022049}],"images_bbox_not_text_overlap":[{"left":0.060634,"top":0.319705,"width":0.016536,"height":0.022049},{"left":0.060634,"top":0.382176,"width":0.016536,"height":0.022049},{"left":0.060634,"top":0.440972,"width":0.016536,"height":0.025723},{"left":0.060634,"top":0.503443,"width":0.016536,"height":0.022049},{"left":0.060634,"top":0.665133,"width":0.016536,"height":0.022049}]}