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Artificial Neural networks are mathematical models at their core. Thistruismpresents some fundamental difficulty when networks are tasked withNatural Language Processing. A key problem lies in measuring the similarity ordistance among vectors in NLP embedding space, since the mathematical conceptof distance does not always agree with the linguistic concept. We suggest thatthe best way to measure linguistic distance among vectors is by employing theLanguage Model (LM) that created them. We introduce Language Model Distance(LMD) for measuring accuracy of vector transformations based on theDistributional Hypothesis ( LMD Accuracy ). We show the efficacy of this metricby applying it to a simple neural network learning the Procrustes algorithm forbilingual word mapping.

Language Model Metrics and Procrustes Analysis for Improved Vector Transformation of NLP Embeddings