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Found 8 papers, 4 papers with code
TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation
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.
Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring
Existing document embedding approaches mainly focus on capturing sequences of words in documents.
Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring
Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation.
Annotating Implicit Reasoning in Arguments with Causal Links
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.
Exploring Methodologies for Collecting High-Quality Implicit Reasoning in Arguments
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.
LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments
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.
IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments
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.
Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation
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.
