This paper presents guidelines for the annotation of deliberate linguistic metaphor. Expressions that contribute to the same metaphorical image are annotated as a chain along with a semantically contrasting expression of the target domain, which helps to make the domain contrast inherent to metaphor more explicit. So far, a corpus of ten TEDx talks with a total of ca. 20k tokens has been annotated according to these guidelines. 1.35% of the tokens are deliberate metaphorical expressions according to our guidelines, which shows that our guidelines successfully identify a significantly higher proportion of deliberate metaphorical expressions than previous studies.

We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or “inked”) notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.