• Corpus ID: 3339724

Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks

@article{Stab2018CrosstopicAM,
  title={Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks},
  author={Christian Stab and Tristan Miller and Iryna Gurevych},
  journal={ArXiv},
  year={2018},
  volume={abs/1802.05758}
}
Argument mining is a core technology for automating argument search in large document collections. [] Key Method We source annotations for over 25,000 instances covering eight controversial topics. The results of cross-topic experiments show that our attention-based neural network generalizes best to unseen topics and outperforms vanilla BiLSTM models by 6% in accuracy and 11% in F-score.

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