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For sequence models with large vocabularies, a majority of network parameterslie in the input and output layers. In this work, we describe a new method,DeFINE, for learning deep token representations efficiently. Our architectureuses a hierarchical structure with novel skip-connections which allows for theuse of low dimensional input and output layers, reducing total parameters andtraining time while delivering similar or better performance versus existingmethods. DeFINE can be incorporated easily in new or existing sequence models.Compared to state-of-the-art methods including adaptive input representations,this technique results in a 6% to 20% drop in perplexity. On WikiText-103,DeFINE reduces the total parameters of Transformer-XL by half with minimalimpact on performance. On the Penn Treebank, DeFINE improves AWD-LSTM by 4points with a 17% reduction in parameters, achieving comparable performance tostate-of-the-art methods with fewer parameters. For machine translation, DeFINEimproves the efficiency of the Transformer model by about 1.4 times whiledelivering similar performance.

DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling