Arabic is the 4th most-used language on the Internet, and its growing presence on social media is providing ample resources for the study of Arabic-language online communities at scale. There are however few tools currently available that can derive valuable insights from this data for decision making, guiding policies, aiding in responses, etc. Is that about to change? The performance of natural language processing (NLP) systems has dramatically improved on tasks such as reading comprehension and natural language inference, and with these advances have come many new application scenarios for the tech. Unsurprisingly, English is where most NLP R&D has been focused.

At the 2019 AWS re:Invent conference, Amazon released Deep Java Library (DJL), an open-source library with Java APIs to simplify training, testing, deploying, and making predictions with deep-learning models. While Java remains the first or second most popular programming language since the late 90s, Python is the most used language for machine learning, with numerous resources and deep-learning frameworks. DJL aims to make deep-learning open-source tools accessible to Java developers, using familiar concepts and intuitive APIs. Java developers can use their favorite IDE with DJL or Jupyter Notebook-based code execution for Java. DJL is framework agnostic; it abstracts away commonly used deep-learning functions, using Java Native Access (JNA) on top of existing deep-learning frameworks, currently providing implementations for Apache MXNet and TensorFlow.