Papers by Nazanin Esmaili
Leveraging Discourse Rewards for Document-Level Neural Machine Translation (2020.coling-main)
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| Challenge: | Document-level machine translation models are often not trained to explicitly ensure discourse quality. |
| Approach: | They propose a method that explicitly optimizes lexical cohesion and coherence metrics by using a reinforcement learning objective. |
| Outcome: | The proposed approach improves document translations over four different languages and three translation domains while maintaining faithfulness to the reference translation. |
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems (N19-1)
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| Challenge: | Existing methods to regularize neural machine translation are limited in low-resource settings. |
| Approach: | They propose a method that uses regressing word embeddings to regularize neural machine translation. |
| Outcome: | The proposed system improves on a strong baseline and a state-of-the-art system. |