@inproceedings{mohtarami-etal-2019-contrastive,
title = "Contrastive Language Adaptation for Cross-Lingual Stance Detection",
author = "Mohtarami, Mitra and
Glass, James and
Nakov, Preslav",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1452",
doi = "10.18653/v1/D19-1452",
pages = "4442--4452",
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.",
}
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%0 Conference Proceedings
%T Contrastive Language Adaptation for Cross-Lingual Stance Detection
%A Mohtarami, Mitra
%A Glass, James
%A Nakov, Preslav
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F mohtarami-etal-2019-contrastive
%X 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.
%R 10.18653/v1/D19-1452
%U https://aclanthology.org/D19-1452
%U https://doi.org/10.18653/v1/D19-1452
%P 4442-4452
Markdown (Informal)
[Contrastive Language Adaptation for Cross-Lingual Stance Detection](https://aclanthology.org/D19-1452) (Mohtarami et al., EMNLP-IJCNLP 2019)
ACL
- Mitra Mohtarami, James Glass, and Preslav Nakov. 2019. Contrastive Language Adaptation for Cross-Lingual Stance Detection. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4442–4452, Hong Kong, China. Association for Computational Linguistics.