Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Weerasooriya, Tharindu"'
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:
Bao, Yujia, Shah, Ankit Parag, Narang, Neeru, Rivers, Jonathan, Maksey, Rajeev, Guan, Lan, Barrere, Louise N., Evenson, Shelley, Basole, Rahul, Miao, Connie, Mehta, Ankit, Boulay, Fabien, Park, Su Min, Pearson, Natalie E., Joy, Eldhose, He, Tiger, Thakur, Sumiran, Ghosal, Koustav, On, Josh, Morrison, Phoebe, Major, Tim, Wang, Eva Siqi, Escobar, Gina, Wei, Jiaheng, Weerasooriya, Tharindu Cyril, Song, Queena, Lashkevich, Daria, Chen, Clare, Kim, Gyuhak, Yin, Dengpan, Hejna, Don, Nomeli, Mo, Wei, Wei
This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performance metrics, and expert insights. Unlike generic LLMs, FALM leverages a
Externí odkaz:
http://arxiv.org/abs/2406.06559
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:
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
We propose a fully Bayesian framework for learning ground truth labels from noisy annotators. Our framework ensures scalability by factoring a generative, Bayesian soft clustering model over label distributions into the classic David and Skene joint
Externí odkaz:
http://arxiv.org/abs/2106.10600
Publikováno v:
Proceedings of the 24th European Conference on Artificial Intelligence 2020
Supervised machine learning often requires human-annotated data. While annotator disagreement is typically interpreted as evidence of noise, population-level label distribution learning (PLDL) treats the collection of annotations for each data item a
Externí odkaz:
http://arxiv.org/abs/2003.07406
Since a tweet is limited to 140 characters, it is ambiguous and difficult for traditional Natural Language Processing (NLP) tools to analyse. This research presents KeyXtract which enhances the machine learning based Stanford CoreNLP Part-of-Speech (
Externí odkaz:
http://arxiv.org/abs/1708.02912
Autor:
Weerasooriya, Tharindu Cyril, Dutta, Sujan, Ranasinghe, Tharindu, Zampieri, Marcos, Homan, Christopher M., KhudaBukhsh, Ashiqur R.
This paper examines social web content moderation from two key perspectives: automated methods (machine moderators) and human evaluators (human moderators). We conduct a noise audit at an unprecedented scale using nine machine moderators trained on w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3084c85b4e0268d143d38b4ca44bfa7d
http://arxiv.org/abs/2301.12534
http://arxiv.org/abs/2301.12534