We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.

Argumentative Link Prediction using Residual Networks and Multi-Objective Learning / Galassi, A.; Lippi, M.; Torroni, P.. - (2018), pp. 1-10. ((Intervento presentato al convegno 5th Workshop on Argument Mining, co-located with EMNLP 2018 tenutosi a bel nel 2018.

Argumentative Link Prediction using Residual Networks and Multi-Objective Learning

Galassi A.;Lippi M.;
2018

Abstract

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.
5th Workshop on Argument Mining, co-located with EMNLP 2018
bel
2018
1
10
Galassi, A.; Lippi, M.; Torroni, P.
Argumentative Link Prediction using Residual Networks and Multi-Objective Learning / Galassi, A.; Lippi, M.; Torroni, P.. - (2018), pp. 1-10. ((Intervento presentato al convegno 5th Workshop on Argument Mining, co-located with EMNLP 2018 tenutosi a bel nel 2018.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1286433
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