  Capturing the similarities between human language units is crucial for
explaining how humans associate different objects, and therefore its
computation has received extensive attention, research, and applications. With
the ever-increasing amount of information around us, calculating similarity
becomes increasingly complex, especially in many cases, such as legal or
medical affairs, measuring similarity requires extra care and precision, as
small acts within a language unit can have significant real-world effects. My
research goal in this thesis is to develop regression models that account for
similarities between language units in a more refined way.
  Computation of similarity has come a long way, but approaches to debugging
the measures are often based on continually fitting human judgment values. To
this end, my goal is to develop an algorithm that precisely catches loopholes
in a similarity calculation. Furthermore, most methods have vague definitions
of the similarities they compute and are often difficult to interpret. The
proposed framework addresses both shortcomings. It constantly improves the
model through catching different loopholes. In addition, every refinement of
the model provides a reasonable explanation. The regression model introduced in
this thesis is called progressively refined similarity computation, which
combines attack testing with adversarial training. The similarity regression
model of this thesis achieves state-of-the-art performance in handling edge
cases.
