Tuesday, July 17, 2018, 6:00 PM – 9:00 PM
Machine Learning & AI Meetup
Matthew Honnibal – Co-Founder, Explosion AI
Embed, Encode, Attend, Predict: A four-step framework for understanding neural network approaches to Natural Language Understanding problems.
While there is a wide literature on developing neural networks for natural language understanding, the networks all have the same general architecture, determined by basic facts about the nature of linguistic input. In this talk I name and explain the four components (embed, encode, attend, predict), give a brief history of approaches to each subproblem, and explain two sophisticated networks in terms of this framework -- one for text classification, and another for textual entailment. The talk assumes a general knowledge of neural networks and machine learning. The talk should be especially suitable for people who have been working on computer vision or other problems.
Ines Montani – Co-Founder, Explosion AI
Prodigy: An annotation tool designed for rapid iteration and developer productivity.
Most developers working with machine learning recognise that data quality and quantity is a more important factor for the success of their project than the specifics of their statistical model. Despite this, it's common for inexperienced teams to make almost no investment into their data. Even amongst more experienced teams, developers often under-estimate the extent to which annotation is a knowledge-based process that requires several iterations to perfect. As a solution, we suggest machine learning developers perform initial annotations themselves, to help them refine the schema. To enable this workflow, we've developed Prodigy, an annotation tool with several features designed to improve productivity. In this talk I'll discuss what we've learned about annotation, and show you how we've implemented these insights into Prodigy.