| Challenge: | Existing MT systems are only useful for information assimilation, and require substantial manual post processing. |
| Approach: | They propose an Interactive Machine Translation interface that assists human translators with on-the-fly hints and suggestions. |
| Outcome: | The proposed interface makes the end-to-end translation process faster, more efficient and creates high-quality translations. |
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Harshita Diddee, Anurag Shukla, Tanuja Ganu, Vivek Seshadri, Sandipan Dandapat, Monojit Choudhury, Kalika Bali
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Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort (2021.acl-long)
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| Challenge: | Multimodal neural machine translation (MNMT) is a task that aims to translate text into the target language using neural networks. |
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| Challenge: | Unsupervised neural machine translation (UNMT) has achieved impressive results, but there are still several challenges for the technology. |
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Miguel Domingo, Mercedes García-Martínez, Amando Estela Pastor, Laurent Bié, Alexander Helle, Álvaro Peris, Francisco Casacuberta, Manuel Herranz Pérez
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Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation (2023.acl-long)
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| Challenge: | In order to improve translation efficiency, human translators perform post-editing on machine translations to correct errors. |
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Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)
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