Understanding the Downstream Instability of Word Embeddings

• URL: http://arxiv.org/abs/2003.04983v1
• Date: Sat, 29 Feb 2020 00:39:12 GMT
• Title: Understanding the Downstream Instability of Word Embeddings
• Authors: Megan Leszczynski, Avner May, Jian Zhang, Sen Wu, Christopher R. Aberger, Christopher R\'e
• Abstract summary: Many industrial machine learning (ML) systems require frequent retraining to keep up-to-date with constantly changing data. Small changes in training data can cause significant changes in the model's predictions. We show how a core building block of modern natural language processing pipelines---pre-trained word embeddings---affects the instability of downstream NLP models.
• Score: 14.373952177486558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many industrial machine learning (ML) systems require frequent retraining to keep up-to-date with constantly changing data. This retraining exacerbates a large challenge facing ML systems today: model training is unstable, i.e., small changes in training data can cause significant changes in the model's predictions. In this paper, we work on developing a deeper understanding of this instability, with a focus on how a core building block of modern natural language processing (NLP) pipelines---pre-trained word embeddings---affects the instability of downstream NLP models. We first empirically reveal a tradeoff between stability and memory: increasing the embedding memory 2x can reduce the disagreement in predictions due to small changes in training data by 5% to 37% (relative). To theoretically explain this tradeoff, we introduce a new measure of embedding instability---the eigenspace instability measure---which we prove bounds the disagreement in downstream predictions introduced by the change in word embeddings. Practically, we show that the eigenspace instability measure can be a cost-effective way to choose embedding parameters to minimize instability without training downstream models, outperforming other embedding distance measures and performing competitively with a nearest neighbor-based measure. Finally, we demonstrate that the observed stability-memory tradeoffs extend to other types of embeddings as well, including knowledge graph and contextual word embeddings.

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