  This paper introduces the novel task of scoring paraphrases for Algebraic
Word Problems (AWP) and presents a self-supervised method for doing so. In the
current online pedagogical setting, paraphrasing these problems is helpful for
academicians to generate multiple syntactically diverse questions for
assessments. It also helps induce variation to ensure that the student has
understood the problem instead of just memorizing it or using unfair means to
solve it. The current state-of-the-art paraphrase generation models often
cannot effectively paraphrase word problems, losing a critical piece of
information (such as numbers or units) which renders the question unsolvable.
There is a need for paraphrase scoring methods in the context of AWP to enable
the training of good paraphrasers. Thus, we propose ParaQD, a self-supervised
paraphrase quality detection method using novel data augmentations that can
learn latent representations to separate a high-quality paraphrase of an
algebraic question from a poor one by a wide margin. Through extensive
experimentation, we demonstrate that our method outperforms existing
state-of-the-art self-supervised methods by up to 32% while also demonstrating
impressive zero-shot performance.
