Kailiang Li

UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings

Natural language processing (NLP) models often require a massive number of parameters for word embeddings, which limits their application on mobile devices. Researchers have employed many approaches, e.g. adaptive inputs, to reduce the parameters of word embeddings. However, existing methods rarely…