The Deep Learning Approach to Natural Language Processing

Deep learning revolution

It’s safe to say that deep learning revolutionized machine learning, especially in fields such as computer vision, speech recognition, and of course, NLP. Deep models created a wave of paradigm shifts in many of the fields of machine learning because deep models learned rich features from raw data instead of using limited human-engineered features. This consequentially caused the pesky and expensive feature engineering to be obsolete. With this, deep models made the traditional workflow more efficient since deep models perform feature learning and task learning simultaneously. Moreover, due to the massive number of parameters (that is, weights) in a deep model, it can encompass significantly more features than a human could’ve engineered.

However, deep models are considered a opaque due to the poor interpretability of the model. For example, understanding the “how” and “what” features learned by deep models for a given problem is still an active area of research. But it’s important to understand that there is a lot more research focusing on the model interpretability of deep learning models.