Lightweight Adaptation of Neural Language Models via Subspace Embedding
This work addresses memory efficiency for multilingual language models, which is an incremental improvement in model compression.
The paper tackles the problem of reducing the memory footprint of pre-trained language models by introducing a compact embedding structure, achieving compression rates beyond 99.8% on XNLI and GLUE benchmarks with a sacrifice of up to 4% absolute accuracy.
Traditional neural word embeddings are usually dependent on a richer diversity of vocabulary. However, the language models recline to cover major vocabularies via the word embedding parameters, in particular, for multilingual language models that generally cover a significant part of their overall learning parameters. In this work, we present a new compact embedding structure to reduce the memory footprint of the pre-trained language models with a sacrifice of up to 4% absolute accuracy. The embeddings vectors reconstruction follows a set of subspace embeddings and an assignment procedure via the contextual relationship among tokens from pre-trained language models. The subspace embedding structure calibrates to masked language models, to evaluate our compact embedding structure on similarity and textual entailment tasks, sentence and paraphrase tasks. Our experimental evaluation shows that the subspace embeddings achieve compression rates beyond 99.8% in comparison with the original embeddings for the language models on XNLI and GLUE benchmark suites.