Argumentative Link Prediction using Residual Networks and Multi-Objective Learning

Andrea Galassi, Marco Lippi, Paolo Torroni


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.
Anthology ID:
W18-5201
Volume:
Proceedings of the 5th Workshop on Argument Mining
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
Noam Slonim, Ranit Aharonov
Venue:
ArgMining
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/W18-5201
DOI:
10.18653/v1/W18-5201
Bibkey:
Cite (ACL):
Andrea Galassi, Marco Lippi, and Paolo Torroni. 2018. Argumentative Link Prediction using Residual Networks and Multi-Objective Learning. In Proceedings of the 5th Workshop on Argument Mining, pages 1–10, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Argumentative Link Prediction using Residual Networks and Multi-Objective Learning (Galassi et al., ArgMining 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5201.pdf
Code
 AGalassi/StructurePrediction18