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    <abstract>Adversarial training has shown impressive success in learning bilingual dictionary without any parallel data by mapping monolingual embeddings to a shared space. However, recent work has shown superior performance for non-adversarial methods in more challenging language pairs. In this work, we revisit adversarial autoencoder for unsupervised word translation and propose two novel extensions to it that yield more stable training and improved results. Our method includes regularization terms to enforce cycle consistency and input reconstruction, and puts the target encoders as an adversary against the corresponding discriminator. Extensive experimentations with European, non-European and low-resource languages show that our method is more robust and achieves better performance than recently proposed adversarial and non-adversarial approaches.</abstract>
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%0 Conference Proceedings
%T Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training
%A Mohiuddin, Tasnim
%A Joty, Shafiq
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F mohiuddin-joty-2019-revisiting
%X Adversarial training has shown impressive success in learning bilingual dictionary without any parallel data by mapping monolingual embeddings to a shared space. However, recent work has shown superior performance for non-adversarial methods in more challenging language pairs. In this work, we revisit adversarial autoencoder for unsupervised word translation and propose two novel extensions to it that yield more stable training and improved results. Our method includes regularization terms to enforce cycle consistency and input reconstruction, and puts the target encoders as an adversary against the corresponding discriminator. Extensive experimentations with European, non-European and low-resource languages show that our method is more robust and achieves better performance than recently proposed adversarial and non-adversarial approaches.
%R 10.18653/v1/N19-1386
%U https://aclanthology.org/N19-1386
%U https://doi.org/10.18653/v1/N19-1386
%P 3857-3867

Markdown (Informal)

[Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training](https://aclanthology.org/N19-1386) (Mohiuddin & Joty, NAACL 2019)

• Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training (Mohiuddin & Joty, NAACL 2019)

ACL

• Tasnim Mohiuddin and Shafiq Joty. 2019. Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3857–3867, Minneapolis, Minnesota. Association for Computational Linguistics.