Pre-trained word embeddings are the primary
 method for transfer learning in several Natural Language Processing (NLP) tasks. Recent
 works have focused on using unsupervised
 techniques such as language modeling to obtain these embeddings. In contrast, this work
 focuses on extracting representations from
 multiple pre-trained supervised models, which
 enriches word embeddings with task and domain specific knowledge. Experiments performed in cross-task, cross-domain and crosslingual settings indicate that such supervised
 embeddings are helpful, especially in the lowresource setting, but the extent of gains is dependent on the nature of the task and domain.
extract contextual embeddings from off-the-shelf supervised model. Helps downstream NLP models in low-resource settings