MIDAS at SemEval-2020 Task 10: Emphasis Selection using Label Distribution Learning and Contextual Embeddings
This work addresses emphasis selection for natural language processing applications, but it is incremental as it builds on existing methods with modest performance gains.
The paper tackled emphasis selection in written text by modeling it as a sequence labeling task using contextual embeddings and label distribution learning, achieving a matching score of 0.783 and ranking 15th out of 31 teams.
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.