  Differing from sentiment transfer, positive reframing seeks to substitute
negative perspectives with positive expressions while preserving the original
meaning. With the emergence of pre-trained language models (PLMs), it is
possible to achieve acceptable results by fine-tuning PLMs. Nevertheless,
generating fluent, diverse and task-constrained reframing text remains a
significant challenge. To tackle this issue, a \textbf{m}ulti-\textbf{s}trategy
\textbf{o}ptimization \textbf{f}ramework (MSOF) is proposed in this paper.
Starting from the objective of positive reframing, we first design positive
sentiment reward and content preservation reward to encourage the model to
transform the negative expressions of the original text while ensuring the
integrity and consistency of the semantics. Then, different decoding
optimization approaches are introduced to improve the quality of text
generation. Finally, based on the modeling formula of positive reframing, we
propose a multi-dimensional re-ranking method that further selects candidate
sentences from three dimensions: strategy consistency, text similarity and
fluency. Extensive experiments on two Seq2Seq PLMs, BART and T5, demonstrate
our framework achieves significant improvements on unconstrained and controlled
positive reframing tasks.
