IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
This addresses the specific task of emphasis selection for visual media, but it is incremental as it applies existing transformer models to a new benchmark.
The paper tackled the problem of selecting words to emphasize in written text for visual media, achieving a Matchm score of 0.810 and ranking third in the SemEval-2020 task.
This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media. We propose an end-to-end model that takes as input the text and corresponding to each word gives the probability of the word to be emphasized. Our results show that transformer-based models are particularly effective in this task. We achieved the best Matchm score (described in section 2.2) of 0.810 and were ranked third on the leaderboard.