Karlo Slot


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SKAM at SemEval-2023 Task 10: Linguistic Feature Integration and Continuous Pretraining for Online Sexism Detection and Classification
Murali Manohar Kondragunta | Amber Chen | Karlo Slot | Sanne Weering | Tommaso Caselli
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

Sexism has been prevalent online. In this paper, we explored the effect of explicit linguistic features and continuous pretraining on the performance of pretrained language models in sexism detection. While adding linguistic features did not improve the performance of the model, continuous pretraining did slightly boost the performance of the model in Task B from a mean macro-F1 score of 0.6156 to 0.6246. The best mean macro-F1 score in Task A was achieved by a finetuned HateBERT model using regular pretraining (0.8331). We observed that the linguistic features did not improve the model’s performance. At the same time, continuous pretraining proved beneficial only for nuanced downstream tasks like Task-B.


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Computational Detection of Narrativity: A Comparison Using Textual Features and Reader Response
Max Steg | Karlo Slot | Federico Pianzola
Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

The task of computational textual narrative detection focuses on detecting the presence of narrative parts, or the degree of narrativity in texts. In this work, we focus on detecting the local degree of narrativity in texts, using short text passages. We performed a human annotation experiment on 325 English texts ranging across 20 genres to capture readers’ perception by means of three cognitive aspects: suspense, curiosity, and surprise. We then employed a linear regression model to predict narrativity scores for 17,372 texts. When comparing our average annotation scores to similar annotation experiments with different cognitive aspects, we found that Pearson’s r ranges from .63 to .75. When looking at the calculated narrative probabilities, Pearson’s r is .91. We found that it is possible to use suspense, curiosity and surprise to detect narrativity. However, there are still differences between methods. This does not imply that there are inherently correct methods, but rather suggests that the underlying definition of narrativity is a determining factor for the results of the computational models employed.