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Antonym-Synonym Classification Based on New Sub-Space Embeddings

AAAI 2019 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Distill pretrained Glove embeddings into synonym and antonym subspacesAntonym-synonym classification · AdjectiveF1: 0.773 -> 0.884Helped
Distill pretrained Glove embeddings into synonym and antonym subspacesAntonym-synonym classification · VerbF1: 0.768 -> 0.891Helped
Distill pretrained Glove embeddings into synonym and antonym subspacesAntonym-synonym classification · NounF1: 0.817 -> 0.844Helped
Distill dLCE embeddings into synonym and antonym subspacesAntonym-synonym classification · AdjectiveF1: 0.897 -> 0.928Helped
Distill dLCE embeddings into synonym and antonym subspacesAntonym-synonym classification · VerbF1: 0.845 -> 0.921Helped
Distill dLCE embeddings into synonym and antonym subspacesAntonym-synonym classification · NounF1: 0.874 -> 0.911Helped
Use random vectors instead of Glove embeddings (ablation)Distiller · AdjectiveF1: 0.884 -> 0.698Hurt
Use random vectors instead of Glove embeddings (ablation)Distiller · VerbF1: 0.891 -> 0.771Hurt
Use random vectors instead of Glove embeddings (ablation)Distiller · NounF1: 0.844 -> 0.702Hurt
Add distributional similarity feature (ablation)Glove Distiller · VerbF1: 0.889 -> 0.891Helped
Add distributional similarity feature (ablation)Glove Distiller · AdjectiveF1: 0.885 -> 0.884No clear effect
Add negation-prefix feature (ablation)Glove Distiller · NounF1: 0.835 -> 0.844Helped

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