{'fraction_non_alphanumeric': 0.016260162601626018, 'fraction_numerical': 0.0016260162601626016, 'mean_word_length': 5.623655913978495, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Towards a Connectionist Argumentation Framework

While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of legal and argumentative reasoning [6]. In this paper, we present a new hybrid model of computation that allows for the deduction and learning of argumentative reasoning. We do so by using Neural-Symbolic Learning Systems to translate argumentation networks into standard neural networks. The approach enables cummulative argumentation through learning, as the strength of the arguments change over time.