Multi-Task Attentive Residual Networks for Argument Mining

24 Feb 2021 Andrea Galassi Marco Lippi Paolo Torroni

We explore the use of residual networks and neural attention for argument mining and in particular link prediction. The method we propose makes no assumptions on document or argument structure... (read more)

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Results from the Paper


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
Relation Classification CDCP ResAttArg Macro F1 42.95 # 1
Link Prediction CDCP ResAttArg F1 29.73 # 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

Methods used in the Paper


METHOD TYPE
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