Papers by Stephanie Brandl
Domain-Specific Word Embeddings with Structure Prediction (2023.tacl-1)
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| Challenge: | Current word embedding methods do not provide a way to use or predict information on structure between sub-corpora, time or domain. |
| Approach: | They propose a word embedding method that provides general word representations for the whole corpus, domain-specific representations and embeddable alignment simultaneously. |
| Outcome: | The proposed method provides better performance than baselines on a dataset of science and philosophy articles. |
Rather a Nurse than a Physician - Contrastive Explanations under Investigation (2023.emnlp-main)
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| Challenge: | a recent study suggests that contrastive explanations are closer to how humans explain a decision than non-contrastive explanations. |
| Approach: | They analyze four English text-classification datasets to determine whether humans explain in contrast to alternatives. |
| Outcome: | The proposed explanations are closer to how humans explain a decision than non-contrastive explanations. |
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze? (2022.acl-long)
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| Challenge: | We compare attention functions in pre-trained language models to human eye fixation patterns during task-specific reading tasks. |
| Approach: | They compare attention functions in large-scale pre-trained language models to classical cognitive models of human attention by using a dataset with eye-tracking recordings of native speakers of English. |
| Outcome: | The proposed model is as predictive of human eye fixation patterns as classical cognitive models of human attention. |
Every word counts: A multilingual analysis of individual human alignment with model attention (2022.aacl-short)
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| Challenge: | Using eye-tracking data, fixation durations are often not considered in generalisation studies because of individual differences. |
| Approach: | They analyse eye-tracking data from speakers of 13 different languages reading . they find significant differences between languages but also individual reading behaviour . |
| Outcome: | The proposed model can be used to improve the generalization of ML models and allow for more personalized and fair applications. |
Identifying Fine-grained Forms of Populism in Political Discourse: A Case Study on Donald Trump’s Presidential Campaigns (2026.eacl-long)
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| Challenge: | Large Language Models excel in a wide range of instruction-following tasks, but their grasp of social science concepts remains underexplored. |
| Approach: | They evaluate pre-trained large language models to identify populist discourse . they use a RoBERTa classifier to analyze campaign speeches by Donald Trump . |
| Outcome: | The proposed model outperforms all new-era instruction-tuned LLMs on populist discourse analysis. |
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)
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Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
| Challenge: | Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages. |
| Approach: | They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices . |
| Outcome: | The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices. |
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations (2024.lrec-main)
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| Challenge: | We compare webcam-based eye-tracking recordings with human-annotated rationales to evaluate importance scores. |
| Approach: | They compare webcam-based eye-tracking recordings with attention-based importance scores for 4 different multilingual Transformer-based language models. |
| Outcome: | The proposed method is comparable to human rationales in linguistic analysis. |
How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns (2022.naacl-main)
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| Challenge: | a recent study shows that gender-neutral pronouns are not associated with processing difficulties . linguistic scholars have observed how technology has altered the course of language evolution . |
| Approach: | They show that gender-neutral pronouns in Danish, English and Swedish are not associated with processing difficulties. |
| Outcome: | a new study shows that gender-neutral pronouns are not associated with human processing difficulties . the findings suggest that such conservativity in language models may limit widespread adoption . |
Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models (2023.emnlp-main)
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| Challenge: | Pretrained machine learning models perpetuate and even amplify existing biases in data . this can result in unfair outcomes that ultimately impact user experience . |
| Approach: | They quantify bias amplification in pretraining and after fine-tuning on vision-and-language models. |
| Outcome: | The results show that pretrained models can perpetuate and even amplify biases in data without compromising performance. |
Llama meets EU: Investigating the European political spectrum through the lens of LLMs (2024.naacl-short)
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| Challenge: | Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. |
| Approach: | They adapt Llama Chat to a European political context and audit its political leanings based on the EUandI questionnaire to analyze its political knowledge and ability to reason in context. |
| Outcome: | The proposed model is adapted from speeches of individual euro-parties from debates in the European Parliament to analyze its political leanings. |