EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1645–1651 Barcelona, Spain (Online), December 12, 2020. 1645 EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media Chandresh S. Kanani Department of CSE Indian Institute of Technology Patna, India cskanani@gmail.com Sriparna Saha Department of CSE Indian Institute of Technology Patna, India sriparna@iitp.ac.in Pushpak Bhattacharyya Department of CSE Indian Institute of Technology Patna, India pb@iitp.ac.in Abstract In visual media, text emphasis is the strengthening of words in a text to convey the intent of the author. Text emphasis in visual media is generally done by using different colors, backgrounds, or font for the text; it helps in conveying the actual meaning of the message to the readers. Emphasis selection is the task of choosing candidate words for emphasis, it helps in automatically designing posters and other media contents with written text. If we consider only the text and do not know the intent, then there can be multiple valid emphasis selections. We propose the use of ensembles for emphasis selection to improve over single emphasis selection models. We show that the use of multi-embedding helps in enhancing the results for base models. To show the efficacy of proposed approach we have also done a comparison of our results with state-of-the-art models. 1 Introduction The SemEval-2020 Task 10 (Shirani et al., 2020) challenge focuses on emphasis selection in visual media. Emphasis is the process of giving importance to some parts of communication to convey the message in a better way. It is used to draw the attention of readers to a specific section of the information. It is also used for removing the ambiguity in the message. In vocal communication, the emphasis is generally conveyed by giving stress on the specific word. In visual communications like flyers, posters, advertisements, the emphasis is conveyed by using different fonts, colors, or backgrounds for the text. Text designing systems like Adobe Spark 1 can automatically provide template-based layouts for text. However, these algorithms generally rely on the visual features of the text like word length and suggest designs based on those features. Sometimes, this type of method does not emphasize on the proper words and might not help in conveying important information or may even convey wrong information. In Fig 1a, we show the automatic design provided by Adobe Spark. Even though Figure 1a is aesthetically pleasing, it is not giving emphasis to essential words and might fail in conveying the message. Instead, Figure 1b uses a different layout and emphasizes on vital words. Given only the text and not the intent of the message, there can be multiple valid emphases, and different authors can prefer to emphasize different words. Therefore, we cannot use a single label to say whether a word should be emphasized or not. To tackle this, we use learning label distribution (LDL) (Gao et al., 2017). LDL is used for assigning a real number to each word showing the probability of a word being …

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