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Traditional neural word embeddings are usually dependent on a richerdiversity of vocabulary. However, the language models recline to cover majorvocabularies via the word embedding parameters, in particular, for multilinguallanguage models that generally cover a significant part of their overalllearning parameters. In this work, we present a new compact embedding structureto reduce the memory footprint of the pre-trained language models with asacrifice of up to 4% absolute accuracy. The embeddings vectors reconstructionfollows a set of subspace embeddings and an assignment procedure via thecontextual relationship among tokens from pre-trained language models. Thesubspace embedding structure calibrates to masked language models, to evaluateour compact embedding structure on similarity and textual entailment tasks,sentence and paraphrase tasks. Our experimental evaluation shows that thesubspace embeddings achieve compression rates beyond 99.8% in comparison withthe original embeddings for the language models on XNLI and GLUE benchmarksuites.

Lightweight Adaptation of Neural Language Models via Subspace Embedding