DeFINE: Deep Factorized Input Word Embeddings for Neural Sequence Modeling
For sequence models with large word-level vocabularies, a majority of network parameters lie in the input and output layers.In this work, we describe a new method, DeFINE, for learning deep word-level representations efficiently.Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods.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 total parameters of Transformer-XL by half with minimal impact on performance.On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters, achieving comparable performance to state-of-the-art methods with fewer parameters.For machine translation, DeFINE improves a Transformer model by 2% while simultaneously reducing total parameters by 26%
DeFINE uses a deep, hierarchical, sparse network with new skip connections to learn better word embeddings efficiently. This paper describes a new method for learning deep word-level representations efficiently by using a hierarchical structure with skip-connections for the use of low dimensional input and output layers.