[1806.05662] GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations(About) Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks.
[1803.11175] Universal Sentence Encoder (2018)(About) models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks.
> With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task
mixes an unsupervised task using a large corpus together with the supervised SNLI task, leveraging the [#Transformer](/tag/attention_is_all_you_need) architecture