@inproceedings{rashid-etal-2019-bilingual,
title = "Bilingual-{GAN}: A Step Towards Parallel Text Generation",
author = "Rashid, Ahmad and
Do-Omri, Alan and
Haidar, Md. Akmal and
Liu, Qun and
Rezagholizadeh, Mehdi",
editor = "Bosselut, Antoine and
Celikyilmaz, Asli and
Ghazvininejad, Marjan and
Iyer, Srinivasan and
Khandelwal, Urvashi and
Rashkin, Hannah and
Wolf, Thomas",
booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2307",
doi = "10.18653/v1/W19-2307",
pages = "55--64",
abstract = "Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is shared between both languages. First two denoising autoencoders are trained, with shared encoders and back-translation to enforce a shared latent state between the two languages. The decoder is shared for the two translation directions. Next, a GAN is trained to generate synthetic {`}code{'} mimicking the languages{'} shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both supervised and unsupervised machine translation.",
}
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<abstract>Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is shared between both languages. First two denoising autoencoders are trained, with shared encoders and back-translation to enforce a shared latent state between the two languages. The decoder is shared for the two translation directions. Next, a GAN is trained to generate synthetic ‘code’ mimicking the languages’ shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both supervised and unsupervised machine translation.</abstract>
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%0 Conference Proceedings
%T Bilingual-GAN: A Step Towards Parallel Text Generation
%A Rashid, Ahmad
%A Do-Omri, Alan
%A Haidar, Md. Akmal
%A Liu, Qun
%A Rezagholizadeh, Mehdi
%Y Bosselut, Antoine
%Y Celikyilmaz, Asli
%Y Ghazvininejad, Marjan
%Y Iyer, Srinivasan
%Y Khandelwal, Urvashi
%Y Rashkin, Hannah
%Y Wolf, Thomas
%S Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F rashid-etal-2019-bilingual
%X Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is shared between both languages. First two denoising autoencoders are trained, with shared encoders and back-translation to enforce a shared latent state between the two languages. The decoder is shared for the two translation directions. Next, a GAN is trained to generate synthetic ‘code’ mimicking the languages’ shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both supervised and unsupervised machine translation.
%R 10.18653/v1/W19-2307
%U https://aclanthology.org/W19-2307
%U https://doi.org/10.18653/v1/W19-2307
%P 55-64
Markdown (Informal)
[Bilingual-GAN: A Step Towards Parallel Text Generation](https://aclanthology.org/W19-2307) (Rashid et al., NAACL 2019)
ACL
- Ahmad Rashid, Alan Do-Omri, Md. Akmal Haidar, Qun Liu, and Mehdi Rezagholizadeh. 2019. Bilingual-GAN: A Step Towards Parallel Text Generation. In Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation, pages 55–64, Minneapolis, Minnesota. Association for Computational Linguistics.