Stab, Christian ; Miller, Tristan ; Schiller, Benjamin ; Rai, Pranav ; Gurevych, Iryna (2018):
Cross-topic Argument Mining from Heterogeneous Sources.
Long Papers, In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3664-3674,
The 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31.10.2018--04.11.2018, [Conference or Workshop Item]
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2018 |
| Creators: | Stab, Christian ; Miller, Tristan ; Schiller, Benjamin ; Rai, Pranav ; Gurevych, Iryna |
| Title: | Cross-topic Argument Mining from Heterogeneous Sources |
| Language: | English |
| 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. |
| Book Title: | Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing |
| Series Volume: | Long Papers |
| Uncontrolled Keywords: | UKP_p_ArgumenText, UKP_a_ArMin |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Ubiquitous Knowledge Processing DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources |
| Event Title: | The 2018 Conference on Empirical Methods in Natural Language Processing |
| Event Location: | Brussels, Belgium |
| Event Dates: | 31.10.2018--04.11.2018 |
| Date Deposited: | 08 Mar 2018 15:30 |
| URL / URN: | http://aclweb.org/anthology/D18-1402 |
| Identification Number: | TUD-CS-2018-0052 |
| PPN: | |
| Corresponding Links: | |
| Projects: | ArgumenText |
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