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    <abstract>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 an evaluation score of 0.810 and were ranked third on the leaderboard.</abstract>
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%0 Conference Proceedings
%T IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
%A Singhal, Vipul
%A Dhull, Sahil
%A Agarwal, Rishabh
%A Modi, Ashutosh
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F singhal-etal-2020-iitk
%X 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 an evaluation score of 0.810 and were ranked third on the leaderboard.
%R 10.18653/v1/2020.semeval-1.217
%U https://aclanthology.org/2020.semeval-1.217
%U https://doi.org/10.18653/v1/2020.semeval-1.217
%P 1665-1670

Markdown (Informal)

[IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection](https://aclanthology.org/2020.semeval-1.217) (Singhal et al., SemEval 2020)

• IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection (Singhal et al., SemEval 2020)

ACL

• Vipul Singhal, Sahil Dhull, Rishabh Agarwal, and Ashutosh Modi. 2020. IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1665–1670, Barcelona (online). International Committee for Computational Linguistics.