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