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.Expand Abstract
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