Search Results for author: Isar Nejadgholi

Found 22 papers, 7 papers with code

Extracting Age-Related Stereotypes from Social Media Texts

no code implementations LREC 2022 Kathleen C. Fraser, Svetlana Kiritchenko, Isar Nejadgholi

Age-related stereotypes are pervasive in our society, and yet have been under-studied in the NLP community.

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

no code implementations25 Jan 2024 Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman

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.

Synthetic Data Generation

Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers

1 code implementation4 Jul 2023 Isar Nejadgholi, Svetlana Kiritchenko, Kathleen C. Fraser, Esma Balkir

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.

Abusive Language Test

ChatGPT for Suicide Risk Assessment on Social Media: Quantitative Evaluation of Model Performance, Potentials and Limitations

1 code implementation15 Jun 2023 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 University of Maryland Reddit suicidality dataset.

Response Generation

The crime of being poor

no code implementations24 Mar 2023 Georgina Curto, Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser

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?

no code implementations14 Feb 2023 Kathleen C. Fraser, Svetlana Kiritchenko, Isar Nejadgholi

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

4 code implementations9 Nov 2022 BigScience Workshop, :, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, Jonathan Tow, Alexander M. Rush, Stella Biderman, Albert Webson, Pawan Sasanka Ammanamanchi, Thomas Wang, Benoît Sagot, Niklas Muennighoff, Albert Villanova del Moral, Olatunji Ruwase, Rachel Bawden, Stas Bekman, Angelina McMillan-Major, Iz Beltagy, Huu Nguyen, Lucile Saulnier, Samson Tan, Pedro Ortiz Suarez, Victor Sanh, Hugo Laurençon, Yacine Jernite, Julien Launay, Margaret Mitchell, Colin Raffel, Aaron Gokaslan, Adi Simhi, Aitor Soroa, Alham Fikri Aji, Amit Alfassy, Anna Rogers, Ariel Kreisberg Nitzav, Canwen Xu, Chenghao Mou, Chris Emezue, Christopher Klamm, Colin Leong, Daniel van Strien, David Ifeoluwa Adelani, Dragomir Radev, Eduardo González Ponferrada, Efrat Levkovizh, Ethan Kim, Eyal Bar Natan, Francesco De Toni, Gérard Dupont, Germán Kruszewski, Giada Pistilli, Hady Elsahar, Hamza Benyamina, Hieu Tran, Ian Yu, Idris Abdulmumin, Isaac Johnson, Itziar Gonzalez-Dios, Javier de la Rosa, Jenny Chim, Jesse Dodge, Jian Zhu, Jonathan Chang, Jörg Frohberg, Joseph Tobing, Joydeep Bhattacharjee, Khalid Almubarak, Kimbo Chen, Kyle Lo, Leandro von Werra, Leon Weber, Long Phan, Loubna Ben allal, Ludovic Tanguy, Manan Dey, Manuel Romero Muñoz, Maraim Masoud, María Grandury, Mario Šaško, Max Huang, Maximin Coavoux, Mayank Singh, Mike Tian-Jian Jiang, Minh Chien Vu, Mohammad A. Jauhar, Mustafa Ghaleb, Nishant Subramani, Nora Kassner, Nurulaqilla Khamis, Olivier Nguyen, Omar Espejel, Ona de Gibert, Paulo Villegas, Peter Henderson, Pierre Colombo, Priscilla Amuok, Quentin Lhoest, Rheza Harliman, Rishi Bommasani, Roberto Luis López, Rui Ribeiro, Salomey Osei, Sampo Pyysalo, Sebastian Nagel, Shamik Bose, Shamsuddeen Hassan Muhammad, Shanya Sharma, Shayne Longpre, Somaieh Nikpoor, Stanislav Silberberg, Suhas Pai, Sydney Zink, Tiago Timponi Torrent, Timo Schick, Tristan Thrush, Valentin Danchev, Vassilina Nikoulina, Veronika Laippala, Violette Lepercq, Vrinda Prabhu, Zaid Alyafeai, Zeerak Talat, Arun Raja, Benjamin Heinzerling, Chenglei Si, Davut Emre Taşar, Elizabeth Salesky, Sabrina J. Mielke, Wilson Y. Lee, Abheesht Sharma, Andrea Santilli, Antoine Chaffin, Arnaud Stiegler, Debajyoti Datta, Eliza Szczechla, Gunjan Chhablani, Han Wang, Harshit Pandey, Hendrik Strobelt, Jason Alan Fries, Jos Rozen, Leo Gao, Lintang Sutawika, M Saiful Bari, Maged S. Al-shaibani, Matteo Manica, Nihal Nayak, Ryan Teehan, Samuel Albanie, Sheng Shen, Srulik Ben-David, Stephen H. Bach, Taewoon Kim, Tali Bers, Thibault Fevry, Trishala Neeraj, Urmish Thakker, Vikas Raunak, Xiangru Tang, Zheng-Xin Yong, Zhiqing Sun, Shaked Brody, Yallow Uri, Hadar Tojarieh, Adam Roberts, Hyung Won Chung, Jaesung Tae, Jason Phang, Ofir Press, Conglong Li, Deepak Narayanan, Hatim Bourfoune, Jared Casper, Jeff Rasley, Max Ryabinin, Mayank Mishra, Minjia Zhang, Mohammad Shoeybi, Myriam Peyrounette, Nicolas Patry, Nouamane Tazi, Omar Sanseviero, Patrick von Platen, Pierre Cornette, Pierre François Lavallée, Rémi Lacroix, Samyam Rajbhandari, Sanchit Gandhi, Shaden Smith, Stéphane Requena, Suraj Patil, Tim Dettmers, Ahmed Baruwa, Amanpreet Singh, Anastasia Cheveleva, Anne-Laure Ligozat, Arjun Subramonian, Aurélie Névéol, Charles Lovering, Dan Garrette, Deepak Tunuguntla, Ehud Reiter, Ekaterina Taktasheva, Ekaterina Voloshina, Eli Bogdanov, Genta Indra Winata, Hailey Schoelkopf, Jan-Christoph Kalo, Jekaterina Novikova, Jessica Zosa Forde, Jordan Clive, Jungo Kasai, Ken Kawamura, Liam Hazan, Marine Carpuat, Miruna Clinciu, Najoung Kim, Newton Cheng, Oleg Serikov, Omer Antverg, Oskar van der Wal, Rui Zhang, Ruochen Zhang, Sebastian Gehrmann, Shachar Mirkin, Shani Pais, Tatiana Shavrina, Thomas Scialom, Tian Yun, Tomasz Limisiewicz, Verena Rieser, Vitaly Protasov, Vladislav Mikhailov, Yada Pruksachatkun, Yonatan Belinkov, Zachary Bamberger, Zdeněk Kasner, Alice Rueda, Amanda Pestana, Amir Feizpour, Ammar Khan, Amy Faranak, Ana Santos, Anthony Hevia, Antigona Unldreaj, Arash Aghagol, Arezoo Abdollahi, Aycha Tammour, Azadeh HajiHosseini, Bahareh Behroozi, Benjamin Ajibade, Bharat Saxena, Carlos Muñoz Ferrandis, Daniel McDuff, Danish Contractor, David Lansky, Davis David, Douwe Kiela, Duong A. Nguyen, Edward Tan, Emi Baylor, Ezinwanne Ozoani, Fatima Mirza, Frankline Ononiwu, Habib Rezanejad, Hessie Jones, Indrani Bhattacharya, Irene Solaiman, Irina Sedenko, Isar Nejadgholi, Jesse Passmore, Josh Seltzer, Julio Bonis Sanz, Livia Dutra, Mairon Samagaio, Maraim Elbadri, Margot Mieskes, Marissa Gerchick, Martha Akinlolu, Michael McKenna, Mike Qiu, Muhammed Ghauri, Mykola Burynok, Nafis Abrar, Nazneen Rajani, Nour Elkott, Nour Fahmy, Olanrewaju Samuel, Ran An, Rasmus Kromann, Ryan Hao, Samira Alizadeh, Sarmad Shubber, Silas Wang, Sourav Roy, Sylvain Viguier, Thanh Le, Tobi Oyebade, Trieu Le, Yoyo Yang, Zach Nguyen, Abhinav Ramesh Kashyap, Alfredo Palasciano, Alison Callahan, Anima Shukla, Antonio Miranda-Escalada, Ayush Singh, Benjamin Beilharz, Bo wang, Caio Brito, Chenxi Zhou, Chirag Jain, Chuxin Xu, Clémentine Fourrier, Daniel León Periñán, Daniel Molano, Dian Yu, Enrique Manjavacas, Fabio Barth, Florian Fuhrimann, Gabriel Altay, Giyaseddin Bayrak, Gully Burns, Helena U. Vrabec, Imane Bello, Ishani Dash, Jihyun Kang, John Giorgi, Jonas Golde, Jose David Posada, Karthik Rangasai Sivaraman, Lokesh Bulchandani, Lu Liu, Luisa Shinzato, Madeleine Hahn de Bykhovetz, Maiko Takeuchi, Marc Pàmies, Maria A Castillo, Marianna Nezhurina, Mario Sänger, Matthias Samwald, Michael Cullan, Michael Weinberg, Michiel De Wolf, Mina Mihaljcic, Minna Liu, Moritz Freidank, Myungsun Kang, Natasha Seelam, Nathan Dahlberg, Nicholas Michio Broad, Nikolaus Muellner, Pascale Fung, Patrick Haller, Ramya Chandrasekhar, Renata Eisenberg, Robert Martin, Rodrigo Canalli, Rosaline Su, Ruisi Su, Samuel Cahyawijaya, Samuele Garda, Shlok S Deshmukh, Shubhanshu Mishra, Sid Kiblawi, Simon Ott, Sinee Sang-aroonsiri, Srishti Kumar, Stefan Schweter, Sushil Bharati, Tanmay Laud, Théo Gigant, Tomoya Kainuma, Wojciech Kusa, Yanis Labrak, Yash Shailesh Bajaj, Yash Venkatraman, Yifan Xu, Yingxin Xu, Yu Xu, Zhe Tan, Zhongli Xie, Zifan Ye, Mathilde Bras, Younes Belkada, Thomas Wolf

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.

Language Modelling Multilingual NLP

Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information

1 code implementation19 Oct 2022 Isar Nejadgholi, Esma Balkir, Kathleen C. Fraser, Svetlana Kiritchenko

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.

Fairness

Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

no code implementations NAACL (TrustNLP) 2022 Esma Balkir, Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser

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) +1

Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors

1 code implementation ACL 2022 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.

Abuse Detection Abusive Language

Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model

no code implementations ACL 2021 Kathleen C. Fraser, Isar Nejadgholi, Svetlana Kiritchenko

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

1 code implementation NAACL (PrivateNLP) 2021 Rajitha Hathurusinghe, Isar Nejadgholi, Miodrag Bolic

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.

Federated Learning NER +1

Towards Ethics by Design in Online Abusive Content Detection

no code implementations28 Oct 2020 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 defined as a supervised classification task.

Ethics General Classification

Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience

no code implementations WS 2020 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 mismatch.

Entity Extraction using GAN NER

Recognizing UMLS Semantic Types with Deep Learning

no code implementations WS 2019 Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine

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.

Entity Linking Relation Extraction +2

Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models

no code implementations3 Oct 2019 Kathleen C. Fraser, Isar Nejadgholi, Berry de Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine

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.

Entity Linking Relation Extraction +2

A NON-LINEAR THEORY FOR SENTENCE EMBEDDING

no code implementations ICLR 2019 Hichem Mezaoui, Isar Nejadgholi

This paper revisits the Random Walk model for sentence embedding in the context of non-extensive statistics.

Sentence Sentence Embedding +1

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