Multiple Word Embeddings for Increased Diversity of Representation

• URL: http://arxiv.org/abs/2009.14394v2
• Date: Fri, 9 Oct 2020 04:40:15 GMT
• Title: Multiple Word Embeddings for Increased Diversity of Representation
• Authors: Brian Lester, Daniel Pressel, Amy Hemmeter, Sagnik Ray Choudhury and Srinivas Bangalore
• Abstract summary: We show a technique that substantially and consistently improves performance over a strong baseline with negligible increase in run time. We analyze aspects of pre-trained embedding similarity and vocabulary coverage and find that the representational diversity is the driving force of why this technique works.
• Score: 15.279850826041066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand. The improvements afforded by these "contextual embeddings" come with a high computational cost. In this work, we explore a simple technique that substantially and consistently improves performance over a strong baseline with negligible increase in run time. We concatenate multiple pre-trained embeddings to strengthen our representation of words. We show that this concatenation technique works across many tasks, datasets, and model types. We analyze aspects of pre-trained embedding similarity and vocabulary coverage and find that the representational diversity between different pre-trained embeddings is the driving force of why this technique works. We provide open source implementations of our models in both TensorFlow and PyTorch.

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