Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
Data exploration is an important step of every data science and machine learning project, including those involving textual data.
We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble.
Ranked #1 on Link Prediction on AbstRCT - Neoplasm
We resolve the latter issue by annotating a Twitter dataset which aims at providing a testing ground on a large unstructured dataset.
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
Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation.
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.
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.