QTP-Net concatenates probabilities from an adaptive Grover circuit with ERNIE embeddings and reports 0.024 average accuracy gain on sentiment classification and 0.784 F1 on word sense disambiguation.
When polysemy matters: Modeling semantic categorization with word embeddings,
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QTP-Net: A Quantum Text Pre-training Network for Natural Language Processing
QTP-Net concatenates probabilities from an adaptive Grover circuit with ERNIE embeddings and reports 0.024 average accuracy gain on sentiment classification and 0.784 F1 on word sense disambiguation.