Based on the assumption that the metaphoricity of a word stems from the context of the whole sentence rather than a single aspect word or POS sequence, I define MD as a sentence-level classification problem, where each sentence within the four datasets is labelled as either “metaphorical” or “literal” (Ma et al. 2021). This approach differs from Kesarwani et al. (2017), who annotate metaphor on a token level. For preprocessing, I reused the cleaned versions of the TroFi and MOH datasets by Su et al. (2020) due to a lack of clear preprocessing information from the Graph project and used the original version of PoFo by Kesarwani et al. In the concatenated dataset there are more literal than metaphorical sentences since it consists of 75% of sentences belonging to TroFi, which suffers from label imbalance, having about 12% more literal than metaphorical examples (figure 1).