  Meta-embedding (ME) learning is an emerging approach that attempts to learn
more accurate word embeddings given existing (source) word embeddings as the
sole input.
  Due to their ability to incorporate semantics from multiple source embeddings
in a compact manner with superior performance, ME learning has gained
popularity among practitioners in NLP.
  To the best of our knowledge, there exist no prior systematic survey on ME
learning and this paper attempts to fill this need.
  We classify ME learning methods according to multiple factors such as whether
they (a) operate on static or contextualised embeddings, (b) trained in an
unsupervised manner or (c) fine-tuned for a particular task/domain.
  Moreover, we discuss the limitations of existing ME learning methods and
highlight potential future research directions.
