Augmenting self-attention with persistent memory vectors allows removal of feed-forward layers from Transformers without degrading performance on character and word level language modeling benchmarks.
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Seq2SQL uses deep learning plus reinforcement learning to generate SQL from natural language, reaching 59.4% execution accuracy on the new WikiSQL dataset of 80k examples.
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Augmenting Self-attention with Persistent Memory
Augmenting self-attention with persistent memory vectors allows removal of feed-forward layers from Transformers without degrading performance on character and word level language modeling benchmarks.