no code implementations • 19 Apr 2024 • Oana Ignat, Xiaomeng Xu, Rada Mihalcea
Using this dataset, we conduct extensive linguistic analyses to (1) compare the AI fake hotel reviews to real hotel reviews, and (2) identify the factors that influence the deception detection model performance.
1 code implementation • 19 Apr 2024 • Oana Ignat, Gayathri Ganesh Lakshmy, Rada Mihalcea
To this end, we compile and make publicly available the InspAIred dataset, which consists of 2, 000 real inspiring posts, 2, 000 real non-inspiring posts, and 2, 000 generated inspiring posts evenly distributed across India and the UK.
1 code implementation • 25 Mar 2024 • Shinka Mori, Oana Ignat, Andrew Lee, Rada Mihalcea
Using GPT-3, we develop HEADROOM, a synthetic dataset of 3, 120 posts about depression-triggering stressors, by controlling for race, gender, and time frame (before and after COVID-19).
1 code implementation • 12 Mar 2024 • Oana Ignat, Longju Bai, Joan Nwatu, Rada Mihalcea
In this paper, we propose methods to identify the data to be annotated to balance model performance and annotation costs.
1 code implementation • 9 Nov 2023 • Joan Nwatu, Oana Ignat, Rada Mihalcea
Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the specific groups that will be impacted by these technologies.
1 code implementation • 5 Nov 2023 • Jingru Yi, Burak Uzkent, Oana Ignat, Zili Li, Amanmeet Garg, Xiang Yu, Linda Liu
While we demonstrate our data augmentation method with MDETR framework, the proposed approach is applicable to common grounding-based vision and language tasks with other frameworks.
1 code implementation • 12 Sep 2023 • Oana Ignat, Santiago Castro, Weiji Li, Rada Mihalcea
We create and make publicly available the ACE (Action Co-occurrencE) dataset, consisting of a large graph of ~12k co-occurring pairs of visual actions and their corresponding video clips.
1 code implementation • 30 May 2023 • Santiago Castro, Oana Ignat, Rada Mihalcea
Joint vision-language models have shown great performance over a diverse set of tasks.
no code implementations • 21 May 2023 • Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
Not surprisingly, this has, in turn, made many NLP researchers -- especially those at the beginning of their careers -- worry about what NLP research area they should focus on.
no code implementations • Findings (ACL) 2022 • Oana Ignat, Jean Maillard, Vishrav Chaudhary, Francisco Guzmán
We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts.
1 code implementation • 16 Feb 2022 • Oana Ignat, Santiago Castro, YuHang Zhou, Jiajun Bao, Dandan Shan, Rada Mihalcea
We consider the task of temporal human action localization in lifestyle vlogs.
1 code implementation • 6 Sep 2021 • Oana Ignat, Y-Lan Boureau, Jane A. Yu, Alon Halevy
We release a dataset of 5, 800 inspiring and 5, 800 non-inspiring English-language public post unique ids collected from a dump of Reddit public posts made available by a third party and use linguistic heuristics to automatically detect which social media English-language posts are inspiring.
1 code implementation • EMNLP 2021 • Oana Ignat, Santiago Castro, Hanwen Miao, Weiji Li, Rada Mihalcea
We aim to automatically identify human action reasons in online videos.
1 code implementation • ACL 2022 • Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan C. Stroud, Rada Mihalcea
We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER -- a novel dataset consisting of 28, 000 videos and descriptions in support of this evaluation framework.
1 code implementation • ACL 2019 • Oana Ignat, Laura Burdick, Jia Deng, Rada Mihalcea
We consider the task of identifying human actions visible in online videos.