conftrace_

Svetlana Kiritchenko

35 papers · 2011–2025 · 7 conferences · across top CS/AI conferences

Achievements

Jump to papers ↓
+12 more ↓ 🏃 Academic Marathon (14) 🌍 Conference Polyglot (7) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (12)
🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (6) 🗺️ Taxonomy Completionist (34) 🔬 Deep Specialist (11) 🤝 Dynamic Duo (16) 🧬 Topic Evolution Prolific Year (5) 💎 Century Club (35) 📈 Trend Setter 🗃️ Keyword Collector (92) 🔥 Unstoppable (13) The Questioner (3)

Conferences

NAACL (11) ACL (10) SEMEVAL (8) EMNLP (3) COLING (1) EACL (1) IJCNLP (1)

Papers

When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text ACL 2025 Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals NAACL 2025 Tackling Social Bias against the Poor: a Dataset and a Taxonomy on Aporophobia NAACL 2025 Fine-Tuning Lowers Safety and Disrupts Evaluation Consistency ACL 2025 How Does Stereotype Content Differ across Data Sources? NAACL 2024 Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes COLING 2024 Examining Gender and Racial Bias in Large Vision–Language Models Using a Novel Dataset of Parallel Images EACL 2024 Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discourse EMNLP 2024 The Crime of Being Poor: Associations between Crime and Poverty on Social Media in Eight Countries NAACL 2024 Aporophobia: An Overlooked Type of Toxic Language Targeting the Poor ACL 2023 What Makes a Good Counter-Stereotype? Evaluating Strategies for Automated Responses to Stereotypical Text ACL 2023 Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers ACL 2023 Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors ACL 2022 Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information EMNLP 2022 Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models NAACL 2022 Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection NAACL 2022 Does Moral Code have a Moral Code? Probing Delphi’s Moral Philosophy NAACL 2022 Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model ACL 2021 Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model IJCNLP 2021 On Cross-Dataset Generalization in Automatic Detection of Online Abuse EMNLP 2020 Big BiRD: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition NAACL 2019 DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning SEMEVAL 2018 SemEval-2018 Task 1: Affect in Tweets SEMEVAL 2018 Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation ACL 2017 Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling NAACL 2016 Sentiment Composition of Words with Opposing Polarities NAACL 2016 SemEval-2016 Task 6: Detecting Stance in Tweets SEMEVAL 2016 SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases SEMEVAL 2016 Sentiment after Translation: A Case-Study on Arabic Social Media Posts NAACL 2015 SemEval-2015 Task 10: Sentiment Analysis in Twitter SEMEVAL 2015 NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews SEMEVAL 2014 An Empirical Study on the Effect of Negation Words on Sentiment ACL 2014 NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets SEMEVAL 2014 NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets SEMEVAL 2013 Lexically-Triggered Hidden Markov Models for Clinical Document Coding ACL 2011