  In many neural models, new features as polynomial functions of existing ones
are used to augment representations. Using the natural language inference task
as an example, we investigate the use of scaled polynomials of degree 2 and
above as matching features. We find that scaling degree 2 features has the
highest impact on performance, reducing classification error by 5% in the best
models.
