{'fraction_non_alphanumeric': 0.014804845222072678, 'fraction_numerical': 0.0, 'mean_word_length': 5.763636363636364, '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}}
Learning to Give Feedback: Modeling Attributes Affecting Argument Persuasiveness in Student Essays

Argument persuasiveness is one of the most important dimensions of argumentative essay quality, yet it is little studied in automated essay scoring research. Using a recently released corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness, we design and train the first set of neural models that predict the persuasiveness of an argument and its attributes in a student essay, enabling useful feedback to be provided to students on why their arguments are (un)persuasive in addition to how persuasive they are.