Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks

Mihir Kale, Aditya Siddhant, Sreyashi Nag, Radhika Parik, Anthony Tomasic, Matthias Grabmair

Keywords: transfer learning, contextual embeddings, meta embeddings

TL;DR: Extract contextual embeddings from off-the-shelf supervised model. Helps downstream NLP models in low-resource settings

Abstract:

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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1906.12039/code)