  This paper presents our submission to the SemEval 2020 - Task 10 on emphasis
selection in written text. We approach this emphasis selection problem as a
sequence labeling task where we represent the underlying text with various
contextual embedding models. We also employ label distribution learning to
account for annotator disagreements. We experiment with the choice of model
architectures, trainability of layers, and different contextual embeddings. Our
best performing architecture is an ensemble of different models, which achieved
an overall matching score of 0.783, placing us 15th out of 31 participating
teams. Lastly, we analyze the results in terms of parts of speech tags,
sentence lengths, and word ordering.
