Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Homan, Christopher"'
Autor:
Keita, Mamadou K., Homan, Christopher, Hamani, Sofiane Abdoulaye, Bremang, Adwoa, Zampieri, Marcos, Alfari, Habibatou Abdoulaye, Ibrahim, Elysabhete Amadou, Owusu, Dennis
Grammatical error correction (GEC) is important for improving written materials for low-resource languages like Zarma -- spoken by over 5 million people in West Africa. Yet it remains a challenging problem. This study compares rule-based methods, mac
Externí odkaz:
http://arxiv.org/abs/2410.15539
Autor:
Dutta, Sujan, Pandita, Deepak, Weerasooriya, Tharindu Cyril, Zampieri, Marcos, Homan, Christopher M., KhudaBukhsh, Ashiqur R.
Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsically subjective, creating a challenging scenario for traditional qualit
Externí odkaz:
http://arxiv.org/abs/2409.12218
Autor:
Pandita, Deepak, Weerasooriya, Tharindu Cyril, Dutta, Sujan, Luger, Sarah K., Ranasinghe, Tharindu, KhudaBukhsh, Ashiqur R., Zampieri, Marcos, Homan, Christopher M.
Human feedback is essential for building human-centered AI systems across domains where disagreement is prevalent, such as AI safety, content moderation, or sentiment analysis. Many disagreements, particularly in politically charged settings, arise b
Externí odkaz:
http://arxiv.org/abs/2408.08411
Autor:
Keita, Mamadou K., Ibrahim, Elysabhete Amadou, Alfari, Habibatou Abdoulaye, Homan, Christopher
Machine translation (MT) is a rapidly expanding field that has experienced significant advancements in recent years with the development of models capable of translating multiple languages with remarkable accuracy. However, the representation of Afri
Externí odkaz:
http://arxiv.org/abs/2406.05888
Autor:
Homan, Christopher Michael, Schrading, J Nicolas, Ptucha, Raymond W, Cerulli, Catherine, Ovesdotter Alm, Cecilia
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 11, p e15347 (2020)
BackgroundSocial media is a rich, virtually untapped source of data on the dynamics of intimate partner violence, one that is both global in scale and intimate in detail. ObjectiveThe aim of this study is to use machine learning and other computatio
Externí odkaz:
https://doaj.org/article/d3d34c6918064ce08fc8ab2a07f2979b
Autor:
Prabhakaran, Vinodkumar, Homan, Christopher, Aroyo, Lora, Davani, Aida Mostafazadeh, Parrish, Alicia, Taylor, Alex, Díaz, Mark, Wang, Ding, Serapio-García, Gregory
Publikováno v:
2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Human annotation plays a core role in machine learning -- annotations for supervised models, safety guardrails for generative models, and human feedback for reinforcement learning, to cite a few avenues. However, the fact that many of these human ann
Externí odkaz:
http://arxiv.org/abs/2311.05074
Autor:
Weerasooriya, Tharindu Cyril, Luger, Sarah, Poddar, Saloni, KhudaBukhsh, Ashiqur R., Homan, Christopher M.
Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media content. Conventionally, annotator disagreements are resolved before any lea
Externí odkaz:
http://arxiv.org/abs/2307.10189
Autor:
Homan, Christopher M., Serapio-Garcia, Greg, Aroyo, Lora, Diaz, Mark, Parrish, Alicia, Prabhakaran, Vinodkumar, Taylor, Alex S., Wang, Ding
Conversational AI systems exhibit a level of human-like behavior that promises to have profound impacts on many aspects of daily life -- how people access information, create content, and seek social support. Yet these models have also shown a propen
Externí odkaz:
http://arxiv.org/abs/2306.11530
Autor:
Aroyo, Lora, Taylor, Alex S., Diaz, Mark, Homan, Christopher M., Parrish, Alicia, Serapio-Garcia, Greg, Prabhakaran, Vinodkumar, Wang, Ding
Machine learning approaches often require training and evaluation datasets with a clear separation between positive and negative examples. This risks simplifying and even obscuring the inherent subjectivity present in many tasks. Preserving such vari
Externí odkaz:
http://arxiv.org/abs/2306.11247
Autor:
Weerasooriya, Tharindu Cyril, Dutta, Sujan, Ranasinghe, Tharindu, Zampieri, Marcos, Homan, Christopher M., KhudaBukhsh, Ashiqur R.
Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators disagree on what is offensive when it comes to real-world social web po
Externí odkaz:
http://arxiv.org/abs/2301.12534