A Collection of Pragmatic-Similarity Judgments over Spoken Dialog
  Utterances
Nigel G. Ward Divette Marco
Automatic measures of similarity between utterances are invaluable for
training speech synthesizers, evaluating machine translation, and assessing
learner productions. While there exist measures for semantic similarity and
prosodic similarity, there are as yet none for pragmatic similarity. To enable
the training of such measures, we developed the first collection of human
judgments of pragmatic similarity between utterance pairs. Each pair consisting
of an utterance extracted from a recorded dialog and a re-enactment of that
utterance. Re-enactments were done under various conditions designed to create
a variety of degrees of similarity. Each pair was rated on a continuous scale
by 6 to 9 judges. The average inter-judge correlation was as high as 0.72 for
English and 0.66 for Spanish. We make this data available at
https://github.com/divettemarco/PragSim .