Search Results for author:
Found 24 papers, 8 papers with code
Extracting Age-Related Stereotypes from Social Media Texts
Age-related stereotypes are pervasive in our society, and yet have been under-studied in the NLP community.
Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification
Particularly in the context of human-in-the-loop systems, high-quality low to mid-range certainty estimates are essential.
Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes
The strategies of counter-facts and broadening universals (i. e., stating that anyone can have a trait regardless of group membership) emerged as the most robust approaches, while humour, perspective-taking, counter-examples, and empathy for the speaker were perceived as less effective.
Projective Methods for Mitigating Gender Bias in Pre-trained Language Models
Mitigation of gender bias in NLP has a long history tied to debiasing static word embeddings.
Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models
To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection.
Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers
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 classification accuracy.
ChatGPT for Suicide Risk Assessment on Social Media: Quantitative Evaluation of Model Performance, Potentials and Limitations
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 University of Maryland Reddit suicidality dataset.
The crime of being poor
The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable.
A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?
As text-to-image systems continue to grow in popularity with the general public, questions have arisen about bias and diversity in the generated images.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information
For a multi-class toxic language classifier, we leverage a concept-based explanation framework to calculate the sensitivity of the model to the concept of sentiment, which has been used before as a salient feature for toxic language detection.
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
In this paper, we briefly review trends in explainability and fairness in NLP research, identify the current practices in which explainability methods are applied to detect and mitigate bias, and investigate the barriers preventing XAI methods from being used more widely in tackling fairness issues.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection
We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection.
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors
Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation.
Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model
In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology.
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning
We trained a BERT-based NER model with WikiPII and showed that with an adequately large training dataset, the model can significantly decrease the cost of manual information extraction, despite the high level of label noise.
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective
The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm.
Towards Ethics by Design in Online Abusive Content Detection
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 defined as a supervised classification task.
On Cross-Dataset Generalization in Automatic Detection of Online Abuse
NLP research has attained high performances in abusive language detection as a supervised classification task.
Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience
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 mismatch.
Recognizing UMLS Semantic Types with Deep Learning
While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0. 90), our results on MedMentions are significantly lower (F1 = 0. 63), suggesting there is still plenty of opportunity for improvement on this new data.
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models
While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0. 90), our results on MedMentions are significantly lower (F1 = 0. 63), suggesting there is still plenty of opportunity for improvement on this new data.
A NON-LINEAR THEORY FOR SENTENCE EMBEDDING
This paper revisits the Random Walk model for sentence embedding in the context of non-extensive statistics.
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content
In response, we focused on training a multi-label classifier to detect both the type and level of toxicity in online content.
