Elman Mansimov


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Towards End-to-End In-Image Neural Machine Translation
Elman Mansimov | Mitchell Stern | Mia Chen | Orhan Firat | Jakob Uszkoreit | Puneet Jain
Proceedings of the First International Workshop on Natural Language Processing Beyond Text

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 for this task inspired by recent approaches to neural machine translation, and demonstrate promising initial results based purely on pixel-level supervision. We then offer a quantitative and qualitative evaluation of our system outputs and discuss some common failure modes. Finally, we conclude with directions for future work.


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Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
Jason Lee | Elman Mansimov | Kyunghyun Cho
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to any sequence generation task. We extensively evaluate the proposed model on machine translation (En-De and En-Ro) and image caption generation, and observe that it significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart.