Papers by Özge Alacam
Modeling Referential Gaze in Task-oriented Settings of Varying Referential Complexity (2022.findings-aacl)
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| Challenge: | Referential gaze is a fundamental phenomenon for psycholinguistics and human-human communication. |
| Approach: | They propose a multimodal NLP task to predict when the gaze is referential . they train a sequential attention-based LSTM model and a transformer encoder architecture to model referential gaze and transfer gaze features to unseen situated settings . |
| Outcome: | The proposed model can be applied to situations with different referential complexities . the proposed model is based on an attention-based LSTM model and a multivariate transformer encoder architecture . |
Prompting Across Time: Evaluating LLMs on Historical and Contemporary Offensive Language (2026.findings-acl)
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| Challenge: | Existing research on large language models and hate speech detection has focused on contemporary data. |
| Approach: | They propose to use a modular prompt design to evaluate early-modern English invectives . they propose to widen the scope of NLP research on hate speech beyond the contemporary domain . |
| Outcome: | The proposed model outperforms a modern hate-speech benchmark on Early Modern English invectives . the results show that the model is more robust to contextual and contextual factors than the current model . |
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models? (2024.findings-eacl)
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| Challenge: | Existing studies show that only the textual component of hateful memes enables the multimodal classifier to generalize across domains while the image component proves highly sensitive to a specific training dataset. |
| Approach: | They propose to use only the textual component of hateful memes to generalize across different domains while the image component is highly sensitive to a specific training dataset. |
| Outcome: | The proposed model performs similarly to hate-meme classifiers in a zero-shot setting, while the introduction of meme’s image captions worsens performance by an average F1 of 0.02. |
POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (2026.findings-acl)
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Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Alexandro Garrido Veliz, P Sam Sahil, Yiran Zhang, Idris Abdulmumin, Marco Antonio Stranisci, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Kritesh Rauniyar, Tanmoy Chakraborty, MD Arfeen Zeeshan, Dheeraj Kodati, Satya Keerthi, Sahar Moradizeyveh, Firoj Alam, Md Arid Hasan, Syed Ishtiaque Ahmed, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Clemencia Siro, Jane Wanjiru Kimani, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam
| Challenge: | polarization is a pervasive threat to democratic institutions, civil discourse, and social cohesion worldwide . most existing datasets focus on English or high-resource languages, reflecting a widespread trend across NLP tasks . |
| Approach: | They propose a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. |
| Outcome: | The proposed dataset analyzes polarization detection, type, and manifestation using a variety of annotation platforms adapted to each cultural context. |
Disentangling Subjectivity and Uncertainty for Hate Speech Annotation and Modeling using Gaze (2025.emnlp-main)
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Özge Alacam, Sanne Hoeken, Andreas Säuberli, Hannes Gröner, Diego Frassinelli, Sina Zarrieß, Barbara Plank
| Challenge: | Variation is inherent in opinion-based annotation tasks like sentiment or hate speech analysis. |
| Approach: | They propose to use annotators' confidence ratings to disentangle subjective variation from uncertainty without relying on specific features present in the data. |
| Outcome: | The proposed approach shows that human gaze patterns offer valuable indicators of subjective evaluation and uncertainty. |
Eyes Don’t Lie: Subjective Hate Annotation and Detection with Gaze (2024.emnlp-main)
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| Challenge: | Hate speech is a complex and subjective phenomenon. |
| Approach: | They propose a dataset that provides gaze data collected in a hate speech annotation experiment and introduce a first gaze-integrated HSD model. |
| Outcome: | The proposed dataset provides gaze data from hate speech annotation experiments. |
Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency (2022.coling-1)
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| Challenge: | Existing evaluations of word/concept representations on verbal fluency tasks rely on human annotations of clusters and switches between sub-categories. |
| Approach: | They analyze word/concept representations in an experimental verbal fluency dataset . they find that ConceptNet embeddings outperforms other semantic representations . |
| Outcome: | The proposed method outperforms other semantic representations by a large margin. |
Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language Models (2023.findings-emnlp)
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| Challenge: | a study comparing static and contextualized language models for subtle semantic shifts in Dutch and English shows that they can detect political connotations and associations. |
| Approach: | They propose a method for detecting subtle semantic shifts between political communities in Dutch and English using static and contextualized language models. |
| Outcome: | The proposed method outperforms static models on a Russian and Spanish task . it relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. |
Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations (2020.lrec-1)
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| Challenge: | Eye4Ref is a rich multimodal dataset of eye-movement recordings from referentially complex situated settings. |
| Approach: | They present a rich multimodal dataset of eye-movement recordings from situated settings . they use linguistic labels, saccadic movement parameters and symbolic knowledge representations . |
| Outcome: | The Eye4Ref dataset is an annotated multimodal dataset from three eyetracking studies on reference resolution and disambiguation tasks in situated settings. |
Hateful Word in Context Classification (2024.emnlp-main)
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| Challenge: | Hate speech detection is a prevalent research field, yet word meaning is underexplored . lexical cues play a role in determining the hatefulness of words, but are not enough in focus for HSD research. |
| Approach: | They propose a Hateful Word in Context Classification task to determine the hatefulness of a word within a specific context. |
| Outcome: | The proposed task aims to determine the hatefulness of a word within a specific context, and argues that definitions prove effective overall, but not in cases where hateful connotations vary. |
MOTIF: Contextualized Images for Complex Words to Improve Human Reading (2022.lrec-1)
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| Challenge: | Existing studies have shown that multimodal information is crucial for concept formation, accordingly for language acquisition. |
| Approach: | They collect a multimodal dataset enriched with complex word annotations and validated image match. |
| Outcome: | The proposed dataset contains 1125 comprehension texts retrieved from Wikipedia Simple Corpus . |