TARGER: Neural Argument Mining at Your Fingertips

Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations  
We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus. The currently available models are pre-trained on three recent argument mining datasets and enable the use of neural argument mining without any reproducibility effort on the user's side. The open source code ensures portability to other domains and use cases, such as an application to search engine ranking that we also describe shortly.
doi:10.18653/v1/p19-3031 dblp:conf/acl/ChernodubOHBHBP19 fatcat:fnehmtjmizgq5itugnjwgzwqke