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This paper introduces the novel task of scoring paraphrases for AlgebraicWord Problems (AWP) and presents a self-supervised method for doing so. In thecurrent online pedagogical setting, paraphrasing these problems is helpful foracademicians to generate multiple syntactically diverse questions forassessments. It also helps induce variation to ensure that the student hasunderstood the problem instead of just memorizing it or using unfair means tosolve it. The current state-of-the-art paraphrase generation models oftencannot effectively paraphrase word problems, losing a critical piece ofinformation (such as numbers or units) which renders the question unsolvable.There is a need for paraphrase scoring methods in the context of AWP to enablethe training of good paraphrasers. Thus, we propose ParaQD, a self-supervisedparaphrase quality detection method using novel data augmentations that canlearn latent representations to separate a high-quality paraphrase of analgebraic question from a poor one by a wide margin. Through extensiveexperimentation, we demonstrate that our method outperforms existingstate-of-the-art self-supervised methods by up to 32% while also demonstratingimpressive zero-shot performance.

'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems