  We present the first openly available multimodal metaphor annotated corpus.
The corpus consists of videos including audio and subtitles that have been
annotated by experts. Furthermore, we present a method for detecting metaphors
in the new dataset based on the textual content of the videos. The method
achieves a high F1-score (62\%) for metaphorical labels. We also experiment
with other modalities and multimodal methods; however, these methods did not
out-perform the text-based model. In our error analysis, we do identify that
there are cases where video could help in disambiguating metaphors, however,
the visual cues are too subtle for our model to capture. The data is available
on Zenodo.
