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Most state-of-the-art models in natural language processing (NLP) are neuralmodels built on top of large, pre-trained, contextual language models thatgenerate representations of words in context and are fine-tuned for the task athand. The improvements afforded by these "contextual embeddings" come with ahigh computational cost. In this work, we explore a simple technique thatsubstantially and consistently improves performance over a strong baseline withnegligible increase in run time. We concatenate multiple pre-trained embeddingsto strengthen our representation of words. We show that this concatenationtechnique works across many tasks, datasets, and model types. We analyzeaspects of pre-trained embedding similarity and vocabulary coverage and findthat the representational diversity between different pre-trained embeddings isthe driving force of why this technique works. We provide open sourceimplementations of our models in both TensorFlow and PyTorch.

Multiple Word Embeddings for Increased Diversity of Representation