To generate convincing and diverse justifications, we developed two models: (1) Ref2Seq which leverages historical justifications as references during generation; and (2) ACMLM, which is an aspect conditional model built on a pre-trained masked language model.
if we encounter a fine-grained aspect, we will replace it with a [MASK] token 30% of the time; while for other words, we will replace them with a [MASK] token 15% of the time.