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Structured Embedding Models for Grouped Data

NeurIPS 2017 · Architectures and efficiency › RNNs and normalization · approach: Learned embeddings for retrieval · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace one global embedding model with group-specific embeddings and shared context vectorsEFE · ArXiv papersheld-out pseudo log-likelihood: -2.176± 0.005 -> -2.287± 0.007Hurt
Replace one global embedding model with group-specific embeddings and shared context vectorsEFE · Senate speechesheld-out pseudo log-likelihood: -2.239 ± 0.002 -> -2.645± 0.002Hurt
Replace one global embedding model with group-specific embeddings and shared context vectorsEFE · Shopping dataheld-out pseudo log-likelihood: -0.772± 0.000 -> -0.770± 0.001Helped
Add a hierarchical prior tying group embeddings to global embeddings (ablation)SEFE · ArXiv papersheld-out pseudo log-likelihood: -2.287± 0.007 -> -2.170± 0.003Helped
Add a hierarchical prior tying group embeddings to global embeddings (ablation)SEFE · Senate speechesheld-out pseudo log-likelihood: -2.645± 0.002 -> -2.217 ± 0.001Helped
Add a hierarchical prior tying group embeddings to global embeddings (ablation)SEFE · Shopping dataheld-out pseudo log-likelihood: -0.770± 0.001 -> -0.767± 0.000Helped
Use feed-forward amortization to generate group embeddings (ablation)SEFE · ArXiv papersheld-out pseudo log-likelihood: -2.287± 0.007 -> -2.153± 0.004Helped
Use feed-forward amortization to generate group embeddings (ablation)SEFE · Senate speechesheld-out pseudo log-likelihood: -2.645± 0.002 -> -2.484± 0.002Helped
Use feed-forward amortization to generate group embeddings (ablation)SEFE · Shopping dataheld-out pseudo log-likelihood: -0.770± 0.001 -> -0.774± 0.000Hurt
Use residual-network rather than feed-forward amortization (ablation)Amortized SEFE · ArXiv papersheld-out pseudo log-likelihood: -2.153± 0.004 -> -2.120± 0.004Helped
Use residual-network rather than feed-forward amortization (ablation)Amortized SEFE · Senate speechesheld-out pseudo log-likelihood: -2.484± 0.002 -> -2.249± 0.002Helped
Use residual-network rather than feed-forward amortization (ablation)Amortized SEFE · Shopping dataheld-out pseudo log-likelihood: -0.774± 0.000 -> -0.762± 0.000Helped

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