# Cisco at AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides using Contextualized Embeddings

arXiv: https://arxiv.org/abs/2101.11422 · HTML: https://mlsanity.com/paper/2101.11422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 10 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Deep learning from crowds](https://mlsanity.com/paper/1709.01779/) (2017)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [Hierarchical Contextualized Representation for Named Entity Recognition](https://mlsanity.com/paper/1911.02257/) (2019)
- [Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings](https://mlsanity.com/paper/1910.08840/) (2019)
- [Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa](https://mlsanity.com/paper/1803.04223/) (2018)
- [Let Me Choose: From Verbal Context to Font Selection](https://mlsanity.com/paper/2005.01151/) (2020)
- [SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media](https://mlsanity.com/paper/2008.03274/) (2020)
- [ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model](https://mlsanity.com/paper/2009.03706/) (2020)
- [IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection](https://mlsanity.com/paper/2007.10820/) (2020)
- [Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides](https://mlsanity.com/paper/2101.03237/) (2021)
- [MIDAS at SemEval-2020 Task 10: Emphasis Selection using Label Distribution Learning and Contextual Embeddings](https://mlsanity.com/paper/2009.02619/) (2020)

## Abstract (arXiv)

This paper describes our proposed system for the AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides. In this specific task, given the contents of a slide we are asked to predict the degree of emphasis to be laid on each word in the slide. We propose 2 approaches to this problem including a BiLSTM-ELMo approach and a transformers based approach based on RoBERTa and XLNet architectures. We achieve a score of 0.518 on the evaluation leaderboard which ranks us 3rd and 0.543 on the post-evaluation leaderboard which ranks us 1st at the time of writing the paper.