  This paper proposes a strategy to assess the robustness of different machine
learning models that involve natural language processing (NLP). The overall
approach relies upon a Search and Semantically Replace strategy that consists
of two steps: (1) Search, which identifies important parts in the text; (2)
Semantically Replace, which finds replacements for the important parts, and
constrains the replaced tokens with semantically similar words. We introduce
different types of Search and Semantically Replace methods designed
specifically for particular types of machine learning models. We also
investigate the effectiveness of this strategy and provide a general framework
to assess a variety of machine learning models. Finally, an empirical
comparison is provided of robustness performance among three different model
types, each with a different text representation.
