Research map / Label-efficient and robust learning / Recognition and long-tail
What Happens Next? Event Prediction Using a Compositional Neural Network Model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn nonlinear neural composition of predicate and arguments plus coherence function (ablation) | WORD2VEC-PRED+ARG · MCNC (Gigaword NYT test) | accuracy: 42.23 -> 49.57 | Helped |
| add argument headword embeddings (ablation) | WORD2VEC-PRED · MCNC (Gigaword NYT test) | accuracy: 40.17 -> 42.23 | Helped |
| add argument embeddings by vector addition (ablation) | MIKOLOV-VERB · MCNC (Gigaword NYT test) | accuracy: 24.57 -> 28.97 | Helped |
| learn predicate-GR embeddings from event chains with word2vec instead of PPMI | C&J08 · MCNC (Gigaword NYT test) | accuracy: 30.52 -> 40.17 | Helped |
| bigram conditional probability instead of PPMI | C&J08 · MCNC (Gigaword NYT test) | accuracy: 30.52 -> 29.67 | No clear effect |
| LSI distributional vectors instead of PPMI | C&J08 · MCNC (Gigaword NYT test) | accuracy: 30.52 -> 27.94 | Hurt |
| autoencoder pretraining of event composition layers (ablation) | EVENT-COMP · MCNC (Gigaword NYT test) | accuracy | No clear effect |
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