We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method 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 Andrea, Lippi Marco, Torroni Paolo. - ELETTRONICO. - (2018), pp. 1-10. (Intervento presentato al convegno 5th Workshop on Argument Mining tenutosi a Brussels, Belgium nel 11/20018) [10.18653/v1/W18-5201].

Argumentative Link Prediction using Residual Networks and Multi-Objective Learning

Galassi Andrea
;
Lippi Marco;Torroni Paolo
2018

Abstract

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method 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.
2018
Proceedings of the 5th Workshop on Argument Mining
1
10
Argumentative Link Prediction using Residual Networks and Multi-Objective Learning / Galassi Andrea, Lippi Marco, Torroni Paolo. - ELETTRONICO. - (2018), pp. 1-10. (Intervento presentato al convegno 5th Workshop on Argument Mining tenutosi a Brussels, Belgium nel 11/20018) [10.18653/v1/W18-5201].
Galassi Andrea, Lippi Marco, Torroni Paolo
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/648213
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 25
  • ???jsp.display-item.citation.isi??? ND
social impact