论文标题
几何感知域的适应性，用于无监督的单词嵌入对齐
Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings
论文作者
论文摘要
我们提出了一种基于新型的基于多种流形的几何方法，用于学习源和目标语言之间单词嵌入的无监督对齐。我们的方法将对齐学习问题制定为在双随机矩阵的多种范围内的域适应问题。该观点源于使两个语言空间的二阶信息对齐的目的。双随机歧管的丰富几何形状允许采用有效的riemannian缀合物梯度算法来进行拟议的配方。从经验上讲，拟议的方法在几种语言对的双语词典诱导任务上的最佳最佳运输方法优于最佳的最佳运输方法。对于遥远的语言对，性能提高更为重要。
We propose a novel manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and the target languages. Our approach formulates the alignment learning problem as a domain adaptation problem over the manifold of doubly stochastic matrices. This viewpoint arises from the aim to align the second order information of the two language spaces. The rich geometry of the doubly stochastic manifold allows to employ efficient Riemannian conjugate gradient algorithm for the proposed formulation. Empirically, the proposed approach outperforms state-of-the-art optimal transport based approach on the bilingual lexicon induction task across several language pairs. The performance improvement is more significant for distant language pairs.
