We construct a preliminary dataset of 6, 000 warrants annotated over 600 arguments for 3 debatable topics.
Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation.
Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA.
Recognizing the implicit link between a claim and a piece of evidence (i. e. warrant) is the key to improving the performance of evidence detection.
This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts.
Existing document embedding approaches mainly focus on capturing sequences of words in documents.
For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established.
In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset.
Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e. g. attack-support relations, stance, explicit premise/claim).
Our coverage result of 74. 6% indicates that argumentative relations can reasonably be explained by our small pattern set.