SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and Implicature
This work provides a resource for researchers in computational linguistics and NLP studying style and pragmatics, though it is incremental as it focuses on data collection rather than novel methods.
The authors tackled the problem of lacking large-scale annotated data for sentence-level linguistic variables by creating SQUINKY!, a corpus of 7,032 sentences rated for formality, informativeness, and implicature on a 1-7 scale, with encouraging reliability in human annotations.
We introduce a corpus of 7,032 sentences rated by human annotators for formality, informativeness, and implicature on a 1-7 scale. The corpus was annotated using Amazon Mechanical Turk. Reliability in the obtained judgments was examined by comparing mean ratings across two MTurk experiments, and correlation with pilot annotations (on sentence formality) conducted in a more controlled setting. Despite the subjectivity and inherent difficulty of the annotation task, correlations between mean ratings were quite encouraging, especially on formality and informativeness. We further explored correlation between the three linguistic variables, genre-wise variation of ratings and correlations within genres, compatibility with automatic stylistic scoring, and sentential make-up of a document in terms of style. To date, our corpus is the largest sentence-level annotated corpus released for formality, informativeness, and implicature.