They filter out noise and select relevant in-domain examples jointly, using similarities between sentence embeddings obtained from the encoder of a bidirectional neural MT system trained on clean indomain data.
we generate negative examples automatically as described in Section 3, and sample a subset to maintain a 1:5 ratio of positive to negative examples.