  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 cross-lingual settings indicate that such supervised
embeddings are helpful, especially in the low-resource setting, but the extent
of gains is dependent on the nature of the task and domain. We make our code
publicly available.
