Evaluate the collective function of references cited together in a given text segment.
Classify their combined role into one of the following categories: 'method', 'background', or 'result'.

Text:They initialize their orthogonal mapping using an adversarial training phase, much like Conneau et al. [2018], and solve the optimization problem with alternating minimization. Our approach differs from theirs: by bootstrapping on the solution of a smaller problem to initialize the mapping, we avoid the need for a neural network initialization that is used in their approach. In addition, our annealing scheme on the entropic regularization leads to smooth convergence, with very little sensitivity to initialization. Concurrently with our work,1 Grave et al. [2018] tackle the problem of unsupervised word embedding alignment with a similar optimization framework as the one proposed by Zhang et al. [2017b], combining Wasserstein distances (an instance of optimal transport distances) and Procrustes alignment. Their approach differs from Zhang et al. [2017b] in how they scale up optimization, by relying on a stochastic Sinkhorn solver [Genevay et al., 2018]. In addition, they initialize their approach by solving a convex relaxation of the the original problem. Although driven by a similar motivation (word embedding alignment) and relying on similar principles (joint optimization of optimal transport coupling and feature mapping) as the work of Zhang et al. [2017b] and Grave et al. [2018], our approach differs from them in several aspects.
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