Paper: Predicting the Fluency of Text with Shallow Structural Features: Case Studies of Machine Translation and Human-Written Text

ACL ID E09-1017
Title Predicting the Fluency of Text with Shallow Structural Features: Case Studies of Machine Translation and Human-Written Text
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

Sentence fluency is an important compo- nent of overall text readability but few studies in natural language processing have sought to understand the factors that define it. We report the results of an ini- tial study into the predictive power of sur- face syntactic statistics for the task; we use fluency assessments done for the purpose of evaluating machine translation. We find that these features are weakly but sig- nificantly correlated with fluency. Ma- chine and human translations can be dis- tinguished with accuracy over 80%. The performance of pairwise comparison of fluency is also very high—over 90% for a multi-layer perceptron classifier. We also test the hypothesis that the learned models capture general fluency properties applica- ble to human-written text. The results do not...