We take<cite> Conneau et al. (2018)</cite> as our baseline, introducing a novel regularizer to enforce cycle consistency. Let X = {x 1 , ..., x n } and Y = {y 1 , ..., y m } be two sets of n and m word embeddings for a source and a target language, respectively. The primal UBLI task aims to learn a linear mapping F : X → Y such that for each x i , F(x i ) corresponds to its translation in Y . Similarly, a linear mapping G : Y → X is defined for the dual task. In addition, we introduce two language discriminators D x and D y , which are trained to discriminate between the mapped word embeddings and the original word embeddings.