Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.
Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today.
Ranked #1 on Extractive Text Summarization on DebateSum
Fact-checking is a journalistic practice that compares a claim made publicly against trusted sources of facts.
Argumentation is arguably one of the central features of scientific language.
Intention identification is a core issue in dialog management.
Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory.
In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling.