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 syllable-and 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.
For example, REF proposed several ways of reusing weights in character-aware NLMs.
Reusing Weights in Subword-aware Neural Language Models
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ArXiv
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