@inproceedings{stab-etal-2018-cross,
title = "Cross-topic Argument Mining from Heterogeneous Sources",
author = "Stab, Christian and
Miller, Tristan and
Schiller, Benjamin and
Rai, Pranav and
Gurevych, Iryna",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1402",
doi = "10.18653/v1/D18-1402",
pages = "3664--3674",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Cross-topic Argument Mining from Heterogeneous Sources
%A Stab, Christian
%A Miller, Tristan
%A Schiller, Benjamin
%A Rai, Pranav
%A Gurevych, Iryna
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F stab-etal-2018-cross
%X 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.
%R 10.18653/v1/D18-1402
%U https://aclanthology.org/D18-1402
%U https://doi.org/10.18653/v1/D18-1402
%P 3664-3674
Markdown (Informal)
[Cross-topic Argument Mining from Heterogeneous Sources](https://aclanthology.org/D18-1402) (Stab et al., EMNLP 2018)
ACL
- Christian Stab, Tristan Miller, Benjamin Schiller, Pranav Rai, and Iryna Gurevych. 2018. Cross-topic Argument Mining from Heterogeneous Sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3664–3674, Brussels, Belgium. Association for Computational Linguistics.