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# SemEval 2020 - Task 10: Emphasis Selection For Written Text in Visual Media

**NOTE: This is a Natural Language Processing task and No experience in Computer Vision or Graphic Design is needed.

## Overview

Visual communication relies heavily on images and short texts. Whether it is flyers, posters, ads, social media posts or motivational messages, it is usually highly designed to grab a viewer’s attention and convey a message in the most efficient way. For text, word emphasis is used to better capture the intent, removing the ambiguity that may exist in plain text. Word Emphasis can clarify or even change the meaning of a sentence by drawing attention to some specific information, and it can be done with Colors, Backgrounds, or Fonts, Italic and Boldface. Our shared task is designed to invite research in this area. We are expecting to see a variety of traditional and modern NLP techniques to model emphasis. Whether you are an expert or new in Natural Language Processing, we encourage you to participate in this fun new task.

The purpose of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring.

Here are some examples from our dataset:

• Hard work never killed a man.
• Never give up on the things that make you smile.
• Throw like a Girl

## Challenges

No additional context from the user or the rest of the design such as background image is provided. The datasets contain very short texts, usually fewer than 10 words. Word emphasis patterns are author- and domain-specific. Without knowing the author’s intent and only considering the input text, multiple emphasis selections are valid. A good model, however, should be able to capture the inter-subjectivity or common sense within the given annotations and finally label words according to higher agreements.

## Important Notes

We will announce the best paper award for each of the following categories:

• The winner(s) of the task – based on the ranking score
• The best system description paper (best results interpretation)
• The best negative results paper

We encourage all teams to describe their submission in a SemEval-2020 paper (ACL format), including teams with negative results.
We encourage all teams to open source their implementations.
During the evaluation phase, only the final valid submissions on CodaLab will be taken as the official submissions to the competition.

## Important Dates

• Trial data ready ----------------- July 31, 2019
• Training data ready ------------- September 4, 2019
• Test data ready ----------------- December 3, 2019
• Evaluation start ----------------- January 10, 2020
• Evaluation end ------------------ January 31, 2020
• Paper submission due ---------- February 23, 2020
• Notification to authors --------- March 29, 2020
• Camera ready due -------------- April 5, 2020
• SemEval workshop ------------- Summer 2020

## Register and Participate

Get started by filling out this form and then register your team at "Participate" tab. You can now download the dataset and evaluation script.

## References

“Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions”, 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)

## Organizers

Reza (Amirreza) Shirani, University of Houston
Thamar Solorio, University of Houston

# Evaluation Criteria

Matchm: For each instance X in the test set Dtest, we select a set Sm(x) of m ∊ (1. . .4) words with the top m probabilities according to the ground truth. Analogously, we select a prediction set set S^m(x) for each m, based on the predicted probabilities.

We define the metric Matchm as follows:

${Match}_m := \frac{\sum_{x \in D_{test}} |S^{(x)}_m \cap \hat{S}^{(x)}_m|/m) }{|D_{test}|}$

### Practice

Start: July 30, 2019, midnight

### Train

Start: Sept. 4, 2019, midnight

### Evaluation

Start: Jan. 10, 2020, midnight

### Post-Evaluation

Start: Jan. 31, 2020, midnight

### Competition Ends

Never

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