Reusing Weights in Subword-aware Neural Language Models

We propose several ways of reusing subword embeddings and other weights in subwordaware neural language models. The proposed techniques do not benefit a competitive character-aware model, but some of them improve the performance of syllableand morpheme-aware models while showing significant reductions in model sizes. We discover a simple hands-on principle: in a multilayer input embedding model, layers should be tied consecutively bottom-up if reused at output. Our best morpheme-aware model with properly reused weights beats the competitive word-level model by a large margin across multiple languages and has 20%–87% fewer parameters.