47 papers with code • 0 benchmarks • 3 datasets
Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
These leaderboards are used to track progress in Argument Mining
Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today.
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.
We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims.
On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline.
We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016).