Randomseed19 at S em E val-2020 Task 10: Emphasis Selection for Written Text in Visual Media (2020)

Abstract: This paper describes our approach to emphasis selection for written text in visual media as a solution for SemEval 2020 Task 10. We used an ensemble of several different Transformer-based models and cast the task as a sequence labeling problem with two tags: ‘I’ as ‘emphasized’ and ‘O’ as ‘non-emphasized’ for each token in the text.

## introduction the purpose of semeval 2020 task 10 is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in short written texts or enabling automated design assistance in authoring. the given task is different from related ones in that word emphasis patterns are person-and domain-specific, making different selections valid depending on the audience and the intent. examples of different emphases are presented in figure 1 . the dataset for this shared task includes short sentences from the following two datasets: the data was labeled by 9 annotators, for further details please refer to the task paper @xcite . this work is licensed under a creative commons attribution 4.0 international licence. licence details: http:// creativecommons.org/licenses/by/4.0/ . we approach the task as a sequence labeling problem with two tags: 'i' as 'emphasized' (the original 'b' was transformed to 'i') and 'o' as 'non-emphasized' for each token in the text. subsequently, the probability of predicted 'i' token was taken as emphasis probability. we considered two ways to convert the given dataset to a sequence labeling dataset: ## experimental setup we used the dataset split scheme provided by the organizers, the data contained 3,877 sentences with 70% training, 10% validation and 20% test sets. all models were trained only on the given train part of the dataset. the technology stack of frameworks and libraries we used 1 : for each set of hyperparameters the model was trained for up to 10 epochs with 4 validations per epoch. we used linear gradient scheduling with warm-up (fraction of warmup steps was set to 0.05 for all models) and adamw optimizer @xcite . for each model we chose two best sets of hyperparameters by: ## conclusion in this paper we presented the system we used in semeval-2020 emphasis selection for written text in visual media competition. the proposed approach is based on transformer-based models and sequence labeling task. the final model we used was an ensemble of 3 models (bert, roberta, xlnet) each trained with best hyperparameters according to validation scores. the model showed good results and took 4th place.