@inproceedings{kandula-min-2021-improving,
title = "Improving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets",
author = "Kandula, Hemanth and
Min, Bonan",
editor = {Vylomova, Ekaterina and
Salesky, Elizabeth and
Mielke, Sabrina and
Lapesa, Gabriella and
Kumar, Ritesh and
Hammarstr{\"o}m, Harald and
Vuli{\'c}, Ivan and
Korhonen, Anna and
Reichart, Roi and
Ponti, Edoardo Maria and
Cotterell, Ryan},
booktitle = "Proceedings of the Third Workshop on Computational Typology and Multilingual NLP",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigtyp-1.4",
doi = "10.18653/v1/2021.sigtyp-1.4",
pages = "32--37",
abstract = "Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora. However, it still suffers from lack of training data for low-resource languages. To tackle this problem, we propose Conditional Language Adversarial Network (CLAN), an end-to-end neural architecture for cross-lingual sentiment analysis without cross-lingual supervision. CLAN differs from prior work in that it allows the adversarial training to be conditioned on both learned features and the sentiment prediction, to increase discriminativity for learned representation in the cross-lingual setting. Experimental results demonstrate that CLAN outperforms previous methods on the multilingual multi-domain Amazon review dataset. Our source code is released at \url{https://github.com/hemanthkandula/clan}.",
}
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<abstract>Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora. However, it still suffers from lack of training data for low-resource languages. To tackle this problem, we propose Conditional Language Adversarial Network (CLAN), an end-to-end neural architecture for cross-lingual sentiment analysis without cross-lingual supervision. CLAN differs from prior work in that it allows the adversarial training to be conditioned on both learned features and the sentiment prediction, to increase discriminativity for learned representation in the cross-lingual setting. Experimental results demonstrate that CLAN outperforms previous methods on the multilingual multi-domain Amazon review dataset. Our source code is released at https://github.com/hemanthkandula/clan.</abstract>
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%0 Conference Proceedings
%T Improving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets
%A Kandula, Hemanth
%A Min, Bonan
%Y Vylomova, Ekaterina
%Y Salesky, Elizabeth
%Y Mielke, Sabrina
%Y Lapesa, Gabriella
%Y Kumar, Ritesh
%Y Hammarström, Harald
%Y Vulić, Ivan
%Y Korhonen, Anna
%Y Reichart, Roi
%Y Ponti, Edoardo Maria
%Y Cotterell, Ryan
%S Proceedings of the Third Workshop on Computational Typology and Multilingual NLP
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F kandula-min-2021-improving
%X Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora. However, it still suffers from lack of training data for low-resource languages. To tackle this problem, we propose Conditional Language Adversarial Network (CLAN), an end-to-end neural architecture for cross-lingual sentiment analysis without cross-lingual supervision. CLAN differs from prior work in that it allows the adversarial training to be conditioned on both learned features and the sentiment prediction, to increase discriminativity for learned representation in the cross-lingual setting. Experimental results demonstrate that CLAN outperforms previous methods on the multilingual multi-domain Amazon review dataset. Our source code is released at https://github.com/hemanthkandula/clan.
%R 10.18653/v1/2021.sigtyp-1.4
%U https://aclanthology.org/2021.sigtyp-1.4
%U https://doi.org/10.18653/v1/2021.sigtyp-1.4
%P 32-37
Markdown (Informal)
[Improving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets](https://aclanthology.org/2021.sigtyp-1.4) (Kandula & Min, SIGTYP 2021)
ACL