WOLFE: An NLP-friendly Declarative Machine Learning Stack

Sameer Singh, Tim Rocktäschel, Luke Hewitt, Jason Naradowsky, Sebastian Riedel

Abstract: Developing machine learning algorithms for natural language processing (NLP) applications is inherently an iterative process, involving a continuous refinement of the choice of model, engineering of features, selection of inference algorithms, search for the right hyperparameters, and error analysis. Existing probabilistic program languages (PPLs) only provide partial solutions; most of them do not support commonly used models such as matrix factorization or neural networks, and do not facilitate interactive and iterative programming that is crucial for rapid development of these models. In this demo we introduce WOLFE, a stack designed to facilitate the development of NLP applications: (1) the WOLFE language allows the user to concisely define complex models, enabling easy modification and extension, (2) the WOLFE interpreter transforms declarative machine learning code into automatically differentiable terms or, where applicable, into factor graphs that allow for complex models to be applied to real-world applications, and (3) the WOLFE IDE provides a number of different visual and interactive elements, allowing intuitive exploration and editing of the data representations, the underlying graphical models, and the execution of the inference algorithms.