To solve this problem, we hypothesize that as human reasoning is guided by innate collection of domain-specific knowledge, it might be beneficial to create such a domain-specific corpus for machines.
Annotation of implicit reasoning (i. e., warrant) in arguments is a critical resource to train models in gaining deeper understanding and correct interpretation of arguments.
The use of argumentation in education has been shown to improve critical thinking skills for end-users such as students, and computational models for argumentation have been developed to assist in this process.
Attacking is not always straightforward and often comprise complex rhetorical moves such that arguers might agree with a logic of an argument while attacking another logic.
In this paper, we define three criteria that a template set should satisfy: expressiveness, informativeness, and uniqueness, and verify the feasibility of creating a template set that satisfies these criteria as a first trial.
Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge.
Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation.
Existing document embedding approaches mainly focus on capturing sequences of words in documents.