Most recent work<cite> (Bao and Bollegala, 2018</cite> ) has focused on the use of an autoencoder (AE) to encode a set of N pretrained embeddings using 3 different variants: (1) Decoupled Autoencoded Meta Embeddings (DAEME) that keep activations separated for each respective embedding input during encoding and uses a reconstruction loss for both predicted embeddings while minimizing the loss for each respective decoded output, (2) Coupled Autoencoded Meta Embeddings (CAEME) which instead learn to predict from a shared encoding and (3) Averaged Autoencoded Meta-Embedding (AAME) is simply an averaging of the embedding set as input instead of using a concatenation.