PTB CTB uas compl uas compl 91.77 45.29 84.54 33.75 221 92.29 46.28 85.11 34.62 124 92.50 46.82 85.62 37.11 71 92.74 48.12 86.00 35.87 39

‘uas’ and ‘compl’ denote unlabeled score and complete match rate respectively (all excluding punctuations).

Systems s uas compl

In particular, we achieve 86.33% uas on CTB which is 1.54% uas improvement over the greedy baseline parser.

UAS : unlabeled attachment score, LAS: labeled attachment score.

Approach UAS ‘ LAS | Time Zhang and Clark (2008) 92.1

For the analysis of the results, we then measured the effectiveness of the constraints using two derived variables: the Collective F unniness (CF) of a message is its mean funniness, while its Upper Agreement ( UA (t)) is the fraction of funniness scores greater than or equal to a given threshold 75.

To rank the generated messages, we take the product of Collective Funniness and Upper Agreement UA (3) and call it the overall Humor Eflectiveness (HE).

The Upper Agreement UA (4) increases from 0.18 to 0.36 and to 0.43, respectively.

‘ parser | iter | UAS ‘ LAS | UEM ‘ arc-standard 23 90.02 87.69 38.33 arc-eager 12 90.18 87.83 40.02 this work 30 91.33 89.16 42.38 arc-standard + easy-first 21 90.49 88.22 39.61 arc-standard + spine 27 90.44 88.23 40.27

Table 2: Accuracy on test set, excluding punctuation, for unlabeled attachment score ( UAS ), labeled attachment score (LAS), unlabeled exact match (UEM).

Considering UAS , our parser provides an improvement of 1.15 over the arc-eager parser and an improvement of 1.31 over the arc-standard parser, that is an error reduction of ~12% and ~13%, respectively.