CM-BART is a metaphor generation model that relies on parallel training data. Creating this data involves three steps: 1. Identify and mask metaphoric verbs using a BERT-based metaphor classification model (e.g., masking "died" in "The house where love had died"), and replace them with infillings from a pre-trained BERT language model. 2. Filter for quality by ensuring that at least 4 out of 5 symbolic beams returned by COMET (Bosselut et al., 2019) are identical. 3. Tag each sentence with FrameNet frames, assuming that the relationship between the frames for the source and target domains reflects a metaphoric mapping.