79 citations · 189 across the 31 of their papers we have counts for
23 papers · 1 filter
Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles
Rongchen Guo, Vincent Francoeur, Isar Nejadgholi +2
Speech Emotion Recognition (SER) is essential for improving human-computer interaction, yet its accuracy remains constrained by the complexity of emotional nuances in speech. In th…
A Taxonomy for Design and Evaluation of Prompt-Based Natural Language Explanations
Isar Nejadgholi, Mona Omidyeganeh, Marc-Antoine Drouin +1
Effective AI governance requires structured approaches for stakeholders to access and verify AI system behavior. With the rise of large language models, Natural Language Explanatio…
Fine-Tuning Lowers Safety and Disrupts Evaluation Consistency
Kathleen C. Fraser, Hillary Dawkins, Isar Nejadgholi +1
Fine-tuning a general-purpose large language model (LLM) for a specific domain or task has become a routine procedure for ordinary users. However, fine-tuning is known to remove th…
Gender-Neutral Machine Translation Strategies in Practice
Hillary Dawkins, Isar Nejadgholi, Chi-kiu Lo
Gender-inclusive machine translation (MT) should preserve gender ambiguity in the source to avoid misgendering and representational harms. While gender ambiguity often occurs natur…
WMT24 Test Suite: Gender Resolution in Speaker-Listener Dialogue Roles
Hillary Dawkins, Isar Nejadgholi, Chi-kiu Lo
We assess the difficulty of gender resolution in literary-style dialogue settings and the influence of gender stereotypes. Instances of the test suite contain spoken dialogue inter…
Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discourse
Rongchen Guo, Isar Nejadgholi, Hillary Dawkins +2
This work provides an explanatory view of how LLMs can apply moral reasoning to both criticize and defend sexist language. We assessed eight large language models, all of which dem…
Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes
Isar Nejadgholi, Kathleen C. Fraser, Anna Kerkhof +1
Gender stereotypes are pervasive beliefs about individuals based on their gender that play a significant role in shaping societal attitudes, behaviours, and even opportunities. Rec…
Projective Methods for Mitigating Gender Bias in Pre-trained Language Models
Hillary Dawkins, Isar Nejadgholi, Daniel Gillis +1
Mitigation of gender bias in NLP has a long history tied to debiasing static word embeddings. More recently, attention has shifted to debiasing pre-trained language models. We stud…
Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models
Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman
Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related da…
Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers
Isar Nejadgholi, Svetlana Kiritchenko, Kathleen C. Fraser +1
Classifiers tend to learn a false causal relationship between an over-represented concept and a label, which can result in over-reliance on the concept and compromised classificati…
ChatGPT for Suicide Risk Assessment on Social Media: Quantitative Evaluation of Model Performance, Potentials and Limitations
Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman
This paper presents a novel framework for quantitatively evaluating the interactive ChatGPT model in the context of suicidality assessment from social media posts, utilizing the Un…
The crime of being poor
Georgina Curto, Svetlana Kiritchenko, Isar Nejadgholi +1
The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable. NGOs and international organizations claim that the poor are blamed for t…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information
Isar Nejadgholi, Esma Balkır, Kathleen C. Fraser +1
Previous works on the fairness of toxic language classifiers compare the output of models with different identity terms as input features but do not consider the impact of other im…
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
Esma Balkir, Svetlana Kiritchenko, Isar Nejadgholi +1
Motivations for methods in explainable artificial intelligence (XAI) often include detecting, quantifying and mitigating bias, and contributing to making machine learning models fa…
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection
Esma Balkir, Isar Nejadgholi, Kathleen C. Fraser +1
We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection. Although feature attribution models usually p…
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors
Isar Nejadgholi, Kathleen C. Fraser, Svetlana Kiritchenko
Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation. New kinds of…
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning
Rajitha Hathurusinghe, Isar Nejadgholi, Miodrag Bolic
We curated WikiPII, an automatically labeled dataset composed of Wikipedia biography pages, annotated for personal information extraction. Although automatic annotation can lead to…
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective
Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser
The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm. Significant effort in Natural Language Processing (NLP) research has been d…
Towards Ethics by Design in Online Abusive Content Detection
Svetlana Kiritchenko, Isar Nejadgholi
To support safety and inclusion in online communications, significant efforts in NLP research have been put towards addressing the problem of abusive content detection, commonly de…
On Cross-Dataset Generalization in Automatic Detection of Online Abuse
Isar Nejadgholi, Svetlana Kiritchenko
NLP research has attained high performances in abusive language detection as a supervised classification task. While in research settings, training and test datasets are usually ob…
Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience
Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn
When comparing entities extracted by a medical entity recognition system with gold standard annotations over a test set, two types of mismatches might occur, label mismatch or span…
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models
Kathleen C. Fraser, Isar Nejadgholi, Berry De Bruijn +3
Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of…