  We re-evaluate the standard practice of sharing weights between input and
output embeddings in state-of-the-art pre-trained language models. We show that
decoupled embeddings provide increased modeling flexibility, allowing us to
significantly improve the efficiency of parameter allocation in the input
embedding of multilingual models. By reallocating the input embedding
parameters in the Transformer layers, we achieve dramatically better
performance on standard natural language understanding tasks with the same
number of parameters during fine-tuning. We also show that allocating
additional capacity to the output embedding provides benefits to the model that
persist through the fine-tuning stage even though the output embedding is
discarded after pre-training. Our analysis shows that larger output embeddings
prevent the model's last layers from overspecializing to the pre-training task
and encourage Transformer representations to be more general and more
transferable to other tasks and languages. Harnessing these findings, we are
able to train models that achieve strong performance on the XTREME benchmark
without increasing the number of parameters at the fine-tuning stage.
