Multi-representation ensembles and delayed SGD updates improve syntax-based NMT (P18-2)
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| Challenge: | Existing studies have shown that NMT models trained to generate target syntax exhibit improved sentence structure relative to those trained on plain-text. |
| Approach: | They propose an approach to decoding ensembles of models generating different representations, focusing on models generating syntax. |
| Outcome: | The proposed approach gives state-of-the-art performance on a difficult Japanese-English task. |
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
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Proceedings of the 3rd Workshop on Neural Generation and Translation (D19-56)
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Multi-agent Learning for Neural Machine Translation (D19-1)
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A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation (2020.aacl-main)
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Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)
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Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages (L18-1)
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