In order to transfer from word-level encoding space to sentence-level encoding space, we introduce a non-linear projection layer on top of the wordlevel context vectorĉ w .
the final context embedding vectorĉ is generated bŷ In total, for the hierarchical co-attention model HCAN: During training, all model parameters θ are jointly learned by minimizing a cross-entropy loss betweenŷ and the true label y. where M is the number of examples in the dataset, N i is the word sense number of i-th example, y ij andŷ ij are the true and predict probability of the i-th example belongs to j-th label.