Cross-topic Argument Mining from Heterogeneous Sources

Christian Stab, Tristan Miller, Benjamin Schiller, Pranav Rai, Iryna Gurevych


Abstract
Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches are designed for use only with specific text types and fall short when applied to heterogeneous texts. In this paper, we propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. We source annotations for over 25,000 instances covering eight controversial topics. We show that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs by more than 3 percentage points in F1 in two- and three-label cross-topic settings. We also show that these results can be further improved by leveraging additional data for topic relevance using multi-task learning.
Anthology ID:
D18-1402
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3664–3674
Language:
URL:
https://aclanthology.org/D18-1402
DOI:
10.18653/v1/D18-1402
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/D18-1402.pdf
Attachment:
 D18-1402.Attachment.zip
Video:
 https://vimeo.com/306127543