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Hierarchical Poset Decoding for Compositional Generalization in Language

NeurIPS 2020 · Large language models › Language, safety and interpretability · approach: Sign and cross-lingual translation · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace poset sketch prediction with Seq2Seq-based sketch prediction (ablation)Hierarchical Poset Decoding · CFQ MCD1accuracy: 79.6% -> 74.3%Hurt
Replace poset sketch prediction with Seq2Seq-based sketch prediction (ablation)Hierarchical Poset Decoding · CFQ MCD2accuracy: 59.6% -> 45.7%Hurt
Replace poset sketch prediction with Seq2Seq-based sketch prediction (ablation)Hierarchical Poset Decoding · CFQ MCD3accuracy: 67.8% -> 50.2%Hurt
Replace poset sketch prediction with Seq2Tree-based sketch prediction (ablation)Hierarchical Poset Decoding · CFQ MCD1accuracy: 79.6% -> 75.7%Hurt
Replace poset sketch prediction with Seq2Tree-based sketch prediction (ablation)Hierarchical Poset Decoding · CFQ MCD2accuracy: 59.6% -> 40.9%Hurt
Replace poset sketch prediction with Seq2Tree-based sketch prediction (ablation)Hierarchical Poset Decoding · CFQ MCD3accuracy: 67.8% -> 51.1%Hurt
Add the hierarchical mechanism (ablation)Basic poset decoding · CFQ MCD1accuracy: 21.3% -> 79.6%Helped
Add the hierarchical mechanism (ablation)Basic poset decoding · CFQ MCD2accuracy: 6.4% -> 59.6%Helped
Add the hierarchical mechanism (ablation)Basic poset decoding · CFQ MCD3accuracy: 10.1% -> 67.8%Helped
Simplify SPARQL expressions by merging similar triplesLSTM+Attention · CFQ MCD1accuracy: 28.9% -> 42.2%Helped
Simplify SPARQL expressions by merging similar triplesTransformer · CFQ MCD1accuracy: 34.9% -> 53.0%Helped

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