  Unavailability of parallel corpora for training text style transfer (TST)
models is a very challenging yet common scenario. Also, TST models implicitly
need to preserve the content while transforming a source sentence into the
target style. To tackle these problems, an intermediate representation is often
constructed that is devoid of style while still preserving the meaning of the
source sentence. In this work, we study the usefulness of Abstract Meaning
Representation (AMR) graph as the intermediate style agnostic representation.
We posit that semantic notations like AMR are a natural choice for an
intermediate representation. Hence, we propose T-STAR: a model comprising of
two components, text-to-AMR encoder and a AMR-to-text decoder. We propose
several modeling improvements to enhance the style agnosticity of the generated
AMR. To the best of our knowledge, T-STAR is the first work that uses AMR as an
intermediate representation for TST. With thorough experimental evaluation we
show T-STAR significantly outperforms state of the art techniques by achieving
on an average 15.2% higher content preservation with negligible loss (3%
approx.) in style accuracy. Through detailed human evaluation with 90,000
ratings, we also show that T-STAR has up to 50% lesser hallucinations compared
to state of the art TST models.
