Contrastive Language Adaptation for Cross-Lingual Stance Detection

Mitra Mohtarami, James Glass, Preslav Nakov


Abstract
We study cross-lingual stance detection, which aims to leverage labeled data in one language to identify the relative perspective (or stance) of a given document with respect to a claim in a different target language. In particular, we introduce a novel contrastive language adaptation approach applied to memory networks, which ensures accurate alignment of stances in the source and target languages, and can effectively deal with the challenge of limited labeled data in the target language. The evaluation results on public benchmark datasets and comparison against current state-of-the-art approaches demonstrate the effectiveness of our approach.
Anthology ID:
D19-1452
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4442–4452
Language:
URL:
https://www.aclweb.org/anthology/D19-1452
DOI:
10.18653/v1/D19-1452
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-1452.pdf