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
- Editors:
- Marta R. Costa-jussà, Enrique Alfonseca
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 195–200
- Language:
- URL:
- https://aclanthology.org/P19-3031
- DOI:
- 10.18653/v1/P19-3031
- Cite (ACL):
- Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, and Alexander Panchenko. 2019. TARGER: Neural Argument Mining at Your Fingertips. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 195–200, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- TARGER: Neural Argument Mining at Your Fingertips (Chernodub et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/P19-3031.pdf
- Code
- achernodub/targer