EigenNoise: A Contrastive Prior to Warm-Start Representations

• URL: http://arxiv.org/abs/2205.04376v1
• Date: Mon, 9 May 2022 15:30:50 GMT
• Title: EigenNoise: A Contrastive Prior to Warm-Start Representations
• Authors: Hunter Scott Heidenreich, Jake Ryland Williams
• Abstract summary: We present a naive scheme for word vectors based on a dense, independent co-occurrence model. We show that our model, EigenNoise, can approach the performance of empirically trained GloVe despite the lack of pre-training data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present a naive initialization scheme for word vectors based on a dense, independent co-occurrence model and provide preliminary results that suggest it is competitive and warrants further investigation. Specifically, we demonstrate through information-theoretic minimum description length (MDL) probing that our model, EigenNoise, can approach the performance of empirically trained GloVe despite the lack of any pre-training data (in the case of EigenNoise). We present these preliminary results with interest to set the stage for further investigations into how this competitive initialization works without pre-training data, as well as to invite the exploration of more intelligent initialization schemes informed by the theory of harmonic linguistic structure. Our application of this theory likewise contributes a novel (and effective) interpretation of recent discoveries which have elucidated the underlying distributional information that linguistic representations capture from data and contrast distributions.

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