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We present a locality preserving loss (LPL) that improves the alignmentbetween vector space embeddings while separating uncorrelated representations.Given two pretrained embedding manifolds, LPL optimizes a model to project anembedding and maintain its local neighborhood while aligning one manifold toanother. This reduces the overall size of the dataset required to align the twoin tasks such as cross-lingual word alignment. We show that the LPL-basedalignment between input vector spaces acts as a regularizer, leading to betterand consistent accuracy than the baseline, especially when the size of thetraining set is small. We demonstrate the effectiveness of LPL optimizedalignment on semantic text similarity (STS), natural language inference (SNLI),multi-genre language inference (MNLI) and cross-lingual word alignment(CLA)showing consistent improvements, finding up to 16% improvement over ourbaseline in lower resource settings.

Locality Preserving Loss: Neighbors that Live together, Align together