Papers by Clara Tump
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation (D18-1)
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| Challenge: | Neural sequence-to-sequence models have proven effective for machine translation, but at the expense of interpretability. |
| Approach: | They analyze how morphological features are captured at different levels of the NMT encoder while varying the target language. |
| Outcome: | The proposed model is not interpretable, but only captures morphological features in context and only to the extent they are directly transferable to the target words. |