@inproceedings{joty-etal-2017-cross,
title = "Cross-language Learning with Adversarial Neural Networks",
author = "Joty, Shafiq and
Nakov, Preslav and
M{\`a}rquez, Llu{\'\i}s and
Jaradat, Israa",
editor = "Levy, Roger and
Specia, Lucia",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-1024",
doi = "10.18653/v1/K17-1024",
pages = "226--237",
abstract = "We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong non-adversarial system.",
}
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%0 Conference Proceedings
%T Cross-language Learning with Adversarial Neural Networks
%A Joty, Shafiq
%A Nakov, Preslav
%A Màrquez, Lluís
%A Jaradat, Israa
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F joty-etal-2017-cross
%X We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong non-adversarial system.
%R 10.18653/v1/K17-1024
%U https://aclanthology.org/K17-1024
%U https://doi.org/10.18653/v1/K17-1024
%P 226-237
Markdown (Informal)
[Cross-language Learning with Adversarial Neural Networks](https://aclanthology.org/K17-1024) (Joty et al., CoNLL 2017)
ACL
- Shafiq Joty, Preslav Nakov, Lluís Màrquez, and Israa Jaradat. 2017. Cross-language Learning with Adversarial Neural Networks. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 226–237, Vancouver, Canada. Association for Computational Linguistics.