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When you create machine learning models in the real world, as opposed to online courses or Kaggle style competitions, you need to generate the training data yourself. This is the first step in any ML project. And as crucial as it is, it is the one we overlook the most. As a freelance ML engineer, I take on new projects often and face the same problem again and again: How can I generate the training data, faster and better? In this article, I want to share some of the best practices I have discovered on the way and help you boost your productivity.

Uber's centre for advanced artificial intelligence research and platforms powers applications in computer vision, natural language processing, deep learning, advanced optimization methods, and intelligent location and sensor processing across the company. The organisation has been doing a lot of researches under these areas for quite a time now. For instance, improving location accuracy with sensing and perception, leveraging computer vision to make Uber safer and efficient, enhancing real-time forecasting, utilising conversational AIs and much more. One important thing to build these models is that they are data-hungry models. Also, producing a large amount of human labelled data is both time-consuming and costlier in manner.

The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training data must be manually generated through a combination of expert domain knowledge and extensive human involvement. However, due to drastic efforts required in annotating text, the resultant datasets are usually small, which severally affects the quality of the learned model, making it hard to generalize. Our work develops an automatic approach for generating training data for event extraction. Our approach allows us to scale up event extraction training instances from thousands to hundreds of thousands, and it does this at a much lower cost than a manual approach. We achieve this by employing distant supervision to automatically create event annotations from unlabelled text using existing structured knowledge bases or tables.We then develop a neural network model with post inference to transfer the knowledge extracted from structured knowledge bases to automatically annotate typed events with corresponding arguments in text.We evaluate our approach by using the knowledge extracted from Freebase to label texts from Wikipedia articles. Experimental results show that our approach can generate a large number of highquality training instances. We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.