Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks
This work addresses the need for better transfer learning methods in NLP, especially for low-resource scenarios, but it is incremental as it builds on existing pre-trained models.
The paper tackled the problem of improving transfer learning in NLP by using supervised embeddings from multiple pre-trained models instead of unsupervised ones, finding that these embeddings are particularly beneficial in low-resource settings, though gains vary by task and domain.
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.