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Modern deep transfer learning approaches have mainly focused on learninggeneric feature vectors from one task that are transferable to other tasks,such as word embeddings in language and pretrained convolutional features invision. However, these approaches usually transfer unary features and largelyignore more structured graphical representations. This work explores thepossibility of learning generic latent relational graphs that capturedependencies between pairs of data units (e.g., words or pixels) fromlarge-scale unlabeled data and transferring the graphs to downstream tasks. Ourproposed transfer learning framework improves performance on various tasksincluding question answering, natural language inference, sentiment analysis,and image classification. We also show that the learned graphs are genericenough to be transferred to different embeddings on which the graphs have notbeen trained (including GloVe embeddings, ELMo embeddings, and task-specificRNN hidden unit), or embedding-free units such as image pixels.

GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations