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Towards End-to-End In-Image Neural Machine Translation

Autor: Orhan Firat, Mia Xu Chen, Jakob Uszkoreit, Elman Mansimov, Mitchell Stern, Puneet Jain

In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing the same text in another language. We propose an end-to-end neural model

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2444f0aaf25c00d8b75130cc82691bfd

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Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation

Autor: Ankur Bapna, Yuan Cao, Aditya Siddhant, Mia Xu Chen, Sneha Kudugunta, Orhan Firat, Naveen Arivazhagan, Yonghui Wu

Publikováno v: ACL

Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c8b2f0a05274e00d6112924d0a9198a
https://doi.org/10.18653/v1/2020.acl-main.252

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Training Deeper Neural Machine Translation Models with Transparent Attention

Autor: Yuan Cao, Mia Xu Chen, Ankur Bapna, Orhan Firat, Yonghui Wu

Publikováno v: EMNLP

While current state-of-the-art NMT models, such as RNN seq2seq and Transformers, possess a large number of parameters, they are still shallow in comparison to convolutional models used for both text and vision applications. In this work we attempt to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff57b462bb875a78785f38d298211fed

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The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation

Autor: Ashish Vaswani, Zhifeng Chen, Mia Xu Chen, Noam Shazeer, Llion Jones, Jakob Uszkoreit, Ankur Bapna, Mike Schuster, Macduff Hughes, Yonghui Wu, George Foster, Melvin Johnson, Niki Parmar, Lukasz Kaiser, Orhan Firat, Wolfgang Macherey

Publikováno v: ACL (1)

The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7f69bd3bf711cd446072c004b5c6789f
https://doi.org/10.18653/v1/p18-1008

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