Edit distance per slot (which we call average edit distance, or AED) for each of the 5 corpora. Lower is better. The table gives the final AED on the test data. Its first 3 columns show the baseline methods just as in Table 3: the trivial deterministic method, the BiLSTM-CRF, and the Attach ablation baseline that attaches the surface punctuation directly to the tree. Column 4 is our method that incorporates a noisy channel, and column 5 (in gray) is our method using oracle (gold) trees. We boldface the best non-oracle result as well as all that are not significantly worse (paired permutation test, p < 0.05). The curves show how our method’s AED (on dev data) varies with the labeled attachment score (LAS) of the trees, where --● at x = 100 uses the oracle (gold) trees,--● at x < 100 uses trees from our parser trained on 100% of the training data, and the ○-- points at x ≪ 100 use increasingly worse parsers. The ♦ and ★ at the right of the graph show the AED of the trivial deterministic baseline and the BiLSTM-CRF baseline, which do not use trees.