Hierarchically-Refined Label Attention Network for Sequence Labeling
Table 9: Supertagging accuracy on CCGbank test set. * indicates that further gains follow from semi-supervised tri-training (improving the accuracy from 94.3% to 94.7%).
['[BOLD] Model', '[BOLD] Accuracy (%)']
CCGBank. In CCG supertagging, the major challenge is a larger set of lexical tags |L| and supertag constraints over long distance dependencies. Xu et al. explore BiRNN-softmax and BiLSTM-softmax, respectively. Lewis et al. train BiLSTM-softmax using tri-training. Vaswani et al. Tu and Gimpel Compared with these methods, BiLSTM-LAN obtains new state-of-the-art results on CCGBank, matching the tri-training performance of Lewis et al.