Training data We train the relabeling function g on another synthetically-noised dataset D drop generated from the manually-labeled data D. To mimic the type distribution of the distantly-labeled examples, we take each example (s, m, t) and randomly drop each type with a fixed rate 0.7 independent of other types to produce a new type set t .
we show in the next section that this is not sufficient for denoising.