@inproceedings{rocha-etal-2018-cross,
title = "Cross-Lingual Argumentative Relation Identification: from {E}nglish to {P}ortuguese",
author = "Rocha, Gil and
Stab, Christian and
Lopes Cardoso, Henrique and
Gurevych, Iryna",
editor = "Slonim, Noam and
Aharonov, Ranit",
booktitle = "Proceedings of the 5th Workshop on Argument Mining",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5217",
doi = "10.18653/v1/W18-5217",
pages = "144--154",
abstract = "Argument mining aims to detect and identify argument structures from textual resources. In this paper, we aim to address the task of argumentative relation identification, a subtask of argument mining, for which several approaches have been recently proposed in a monolingual setting. To overcome the lack of annotated resources in less-resourced languages, we present the first attempt to address this subtask in a cross-lingual setting. We compare two standard strategies for cross-language learning, namely: projection and direct-transfer. Experimental results show that by using unsupervised language adaptation the proposed approaches perform at a competitive level when compared with fully-supervised in-language learning settings.",
}
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%0 Conference Proceedings
%T Cross-Lingual Argumentative Relation Identification: from English to Portuguese
%A Rocha, Gil
%A Stab, Christian
%A Lopes Cardoso, Henrique
%A Gurevych, Iryna
%Y Slonim, Noam
%Y Aharonov, Ranit
%S Proceedings of the 5th Workshop on Argument Mining
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F rocha-etal-2018-cross
%X Argument mining aims to detect and identify argument structures from textual resources. In this paper, we aim to address the task of argumentative relation identification, a subtask of argument mining, for which several approaches have been recently proposed in a monolingual setting. To overcome the lack of annotated resources in less-resourced languages, we present the first attempt to address this subtask in a cross-lingual setting. We compare two standard strategies for cross-language learning, namely: projection and direct-transfer. Experimental results show that by using unsupervised language adaptation the proposed approaches perform at a competitive level when compared with fully-supervised in-language learning settings.
%R 10.18653/v1/W18-5217
%U https://aclanthology.org/W18-5217
%U https://doi.org/10.18653/v1/W18-5217
%P 144-154
Markdown (Informal)
[Cross-Lingual Argumentative Relation Identification: from English to Portuguese](https://aclanthology.org/W18-5217) (Rocha et al., ArgMining 2018)
ACL