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Graphix-T5: Mixing Pre-trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing

AAAI 2023 · Architectures and efficiency › Attention and transformers · approach: Transformer expressivity and learnability · 88 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add Graphix graph-aware layers to the T5 encoderT5-large · Spider devEM: 67.0 -> 72.7Helped
Add Graphix graph-aware layers to the T5 encoderT5-large · Spider devEX: 69.3 -> 75.9Helped
Add Graphix graph-aware layers to the T5 encoderT5-3B · Spider devEM: 71.5 -> 75.6Helped
Add Graphix graph-aware layers to the T5 encoderT5-3B · SynEM: 58.0 -> 66.9Helped
Add Graphix graph-aware layers to the T5 encoderT5-3B · DkEM: 46.9 -> 51.2Helped
Add Graphix graph-aware layers to the T5 encoderT5-3B · RealisticEM: 62.0 -> 72.4Helped
Add Graphix graph-aware layers to the T5 encoderT5-3B · Spider-SSP TemplateEM: 64.8 -> 70.1Helped
Add constrained decoding with PICARDGraphix-T5-3B · Spider devEM: 75.6 -> 77.1Helped
Replace Bridge Mode with No-Match Mode (ablation)Graphix-T5-large · Spider devEM: 72.7 -> 71.1Hurt
Replace Bridge Mode with No-Match Mode (ablation)Graphix-T5-large · Spider devEX: 75.9 -> 74.2Hurt
Add Graphix layers to the decoder in addition to the encoder (ablation)Graphix-T5-large · Spider devEM: 72.7 -> 72.0Hurt
Add Graphix layers to the decoder in addition to the encoder (ablation)Graphix-T5-large · Spider devEX: 75.9 -> 74.7Hurt

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