In this tutorial, I will present the basic concepts of neural MT as the new state-of-the-art in automatic translation. We will look at the common architecture of attention-based sequence-to-se quence models and include an overview of useful extensions and practical tricks. The tutorial will mention available tools and resources to make it easy to get started with hands-on experience on real-world data. We do not require any deep understanding of neural networks and machine learning as a background but focus on a gentle introduction of models and techniques.

In the last 10-15 years, NLP techniques have proven effective in a number of forensic applications, such as author profiling and deception detection. The tutorial will show the experience of DeCour - DEception in COURts -, a corpus constituted by transcripts of hearings held in four Italian courts, which was employed for a typical task of text classification carried out thorough stylometric techniques, in order to distinguish false from truthful statements. The process will be examined with particular attention to the methods employed, from the data collection, through the preprocessing and the feature selection. In the end, the data analysis and the results will be discussed.

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