PolyBERT combines a poly-encoder cross-attention fusion of token and sequence semantics with batch contrastive learning, achieving 81.0 all-words F1 and 37.6% training time savings.
Sense vocabulary compression through the semantic knowledge of wordnet for neural word sense disambiguation,
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PolyBERT: Fine-Tuned Poly Encoder BERT-Based Model for Word Sense Disambiguation
PolyBERT combines a poly-encoder cross-attention fusion of token and sequence semantics with batch contrastive learning, achieving 81.0 all-words F1 and 37.6% training time savings.