Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings

• URL: http://arxiv.org/abs/2004.08243v2
• Date: Mon, 20 Apr 2020 14:48:45 GMT
• Title: Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings
• Authors: Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra
• Abstract summary: We propose a novel manifold based geometric approach for learning unsupervised alignment of word embedding between the source and the target languages. Our approach formulates the alignment learning problem as a domain adaptation problem over the manifolds bilingual doubly matrices. Empirically, the proposed approach outperforms state-of-the-art optimal transport based approach on the gradient induction task across several language pairs.
• Score: 15.963615360741356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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