On the scaling of polynomial features for representation matching.

In many neural models, new features as polynomial functions of existing onesare used to augment representations. Using the natural language inference taskas an example, we investigate the use of scaled polynomials of degree 2 andabove as matching features. We find that scaling degree 2 features has thehighest impact on performance, reducing classification error by 5% in the bestmodels.