| Challenge: | large pre-trained language models are impractical for on-device applications due to their size and inference cost. |
| Approach: | They propose an embedding-free MLP-Mixer model for on-device NLP that achieves high weight-efficiency thanks to a novel projection layer. |
| Outcome: | The proposed model beats state-of-the-art of tiny models by 97.8% on two datasets . it beats mBERT on MTOP and multiATIS, while using 170x less parameters . |
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Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information (2020.emnlp-main)
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