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

MIDAS at SemEval-2020 Task 10: Emphasis Selection using Label Distribution Learning and Contextual Embeddings