IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection

• URL: http://arxiv.org/abs/2007.10820v1
• Date: Tue, 21 Jul 2020 14:05:56 GMT
• Title: IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
• Authors: Vipul Singhal, Sahil Dhull, Rishabh Agarwal and Ashutosh Modi
• Abstract summary: 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.
• Score: 8.352123313770552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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