On the other hand, AM has rapidlyfrom a given document (Lippi 2016). Recent years have seen the development evolved by exploiting state-of-the-art neural architectures of a large number of techniques in this area, on coming from deep learning. So far, the wake of the advancements produced by deep these two worlds have progressed largely independently learning on the whole research field of natural of each other. Only recently, a few works language processing (NLP). Yet, it is widely recognized have taken some steps towards the integration of that the existing AM systems still have such methods, by applying techniques combining a large margin of improvement, as good results sub-symbolic classifiers with knowledge expressed have been obtained with some genres where prior in the form of rules and constraints to AM. knowledge on the structure of the text eases some Niculae et al. (2017) adopted structuredFor instance, AM tasks, but other genres such as legal cases support vector machines and recurrent neural and social media documents still require more networks to collectively classify argument components work (Cabrio and Villata, 2018). Performing and and their relations in short documents, understanding argumentation requires advanced by hard-coding contextual dependencies and constraints reasoning capabilities that are natural skills for humans, of the argument model in a factor graph. but which are difficult to learn for a machine. A joint inference approach for argument component Understanding whether a given piece of classification and relation identification was evidence supports a given claim, or whether two Persing and Ng (2016), followinginstead proposed by claims attack each other, are complex problems a pipeline scheme where integer linear programming that humans are able to address thanks to their is used to enforce mathematical constraints ability to exploit commonsense knowledge, and to on the outcomes of a first-stage set of classifiers.

Bayesian networks are a central tool in machine learning and artificial intelligence, and make use of conditional independencies to impose structure on joint distributions. However, they are generally not as expressive as deep learning models and inference is hard and slow. In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but use little interpretable structure. Here, we extend the notion of SPNs towards conditional distributions, which combine simple conditional models into high-dimensional ones. As shown in our experiments, the resulting conditional SPNs can be naturally used to impose structure on deep probabilistic models, allow for mixed data types, while maintaining fast and efficient inference.