Multi-Task Attentive Residual Networks for Argument Mining
We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesting compromise between generality, performance accuracy and reduced model size.
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Datasets
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Relation Classification | AbstRCT - Neoplasm | ResAttArg | Macro F1 | 70.92 | # 1 | |
| Link Prediction | AbstRCT - Neoplasm | ResAttArg | F1 | 54.43 | # 1 | |
| Link Prediction | CDCP | ResAttArg | F1 | 29.73 | # 1 | |
| Relation Classification | CDCP | ResAttArg | Macro F1 | 42.95 | # 1 | |
| Component Classification | CDCP | ResAttArg | Macro F1 | 78.71 | # 1 | |
| Link Prediction | DRI Corpus | ResAttArg | F1 | 43.66 | # 1 | |
| Relation Classification | DRI Corpus | ResAttArg | Macro F1 | 37.72 | # 1 |
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