Papers › Simple Recurrent Units for Highly Parallelizable Recurrence
Simple Recurrent Units for Highly Parallelizable Recurrence
Tao Lei, Yu Zhang, Sida I. Wang, Hui Dai, Yoav Artzi
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model on translation by incorporating SRU into the architecture.
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Code
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0c93830510f645a3 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Machine Translation | WMT2014 English-German | Transformer + SRU | BLEU score | 28.4 | #45 of 91 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-German | Transformer + SRU | Hardware Burden | 34G | #45 of 91 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | SRU | EM | 71.4 | #150 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | SRU | F1 | 80.2 | #150 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | SRU | Hardware Burden | 4G | #150 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | SRU | EM | 71.4 | #32 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | SRU | F1 | 80.2 | #32 of 55 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: SRU
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