Minor changes make a difference: a case study on the consistency of UD-based dependency parsers
This work addresses consistency issues in dependency parsers for NLP applications, but it is incremental as it focuses on a specific bias.
The study investigated how minor input changes, such as replacing numerals, affect the consistency of dependency parsers across four languages, finding that these changes cause large output differences and suggesting data augmentation as a solution.
Many downstream applications are using dependency trees, and are thus relying on dependency parsers producing correct, or at least consistent, output. However, dependency parsers are trained using machine learning, and are therefore susceptible to unwanted inconsistencies due to biases in the training data. This paper explores the effects of such biases in four languages - English, Swedish, Russian, and Ukrainian - though an experiment where we study the effect of replacing numerals in sentences. We show that such seemingly insignificant changes in the input can cause large differences in the output, and suggest that data augmentation can remedy the problems.