TARGER: Neural Argument Mining at Your Fingertips

Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko


Abstract
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.
Anthology ID:
P19-3031
Original:
P19-3031v1
Version 2:
P19-3031v2
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
195–200
Language:
URL:
https://www.aclweb.org/anthology/P19-3031
DOI:
10.18653/v1/P19-3031
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-3031.pdf