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

arXiv 2101.11422 ↗First posted 10 Jan 2021cs.CL cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 10 Jan 2021.

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15 of 23 references.

References · 15

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  3. 2018Deep contextualized word representationscited by 2,285
  4. 2014Label Distribution Learningcited by 122
  5. 2017Deep learning from crowdscited by 105
  6. 2017SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publicationscited by 101
  7. 2019Hierarchical Contextualized Representation for Named Entity Recognitioncited by 32
  8. 2019Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddingscited by 14
  9. 2018Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexacited by 11
  10. 2020Let Me Choose: From Verbal Context to Font Selectioncited by 8
  11. 2020SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Mediacited by 3
  12. 2020ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Modelcited by 3
3 more
  1. 2020IITK at SemEval-2020 Task 10: Transformers for Emphasis Selectioncited by 2
  2. 2021Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slidescited by 2
  3. 2020MIDAS at SemEval-2020 Task 10: Emphasis Selection using Label Distribution Learning and Contextual Embeddingscited by 1

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