More Effective Transfer Learning for NLP - Indico

This spring I presented a talk entitled "Effective Transfer Learning for NLP" at ODSC East. The talk was intended to demonstrate how surprisingly effective pre-trained word and document embeddings are at low training data volumes, and to lay out a set of practical recommendations for applying these techniques to your own tasks. Thanks to some excellent research by Alec Radford and the team at OpenAI, our recommendations are beginning to change. To explain why the tides are shifting, let's first walk through the rubric we use at Indico to evaluate whether or not a novel machine learning method is viable for industry use. Let's see how well pre-trained word document embeddings satisfy these requirements: In short, using pre-trained embeddings is computationally cheap and performs well at the lower extremes of training data availability, but using static representations imposes an unfortunate cap on the benefit gained from additional training data.