Papers by Parnia Bahar
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation (D18-1)
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| Challenge: | Existing models treat source and target sentences as one-dimensional sequences over time, while a 2D mapping is achieved using an MDLSTM layer. |
| Approach: | They propose a multi-dimensional long short-term memory architecture for translation modelling that uses an MDLSTM layer to define the correspondence between source and target words. |
| Outcome: | The proposed model improves on two WMT 2017 tasks, showing that the source and target sentences are aligned with each other in a 2D grid. |
Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture (2020.acl-main)
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| Challenge: | Current neural networks are heavily growing in depth, with many fully connected layers. |
| Approach: | They propose to combine stabilized lottery ticket pruning with unstructured pruning to improve model performance. |
| Outcome: | The proposed pruning techniques outperform all other techniques for even higher sparsity levels. |