no code implementations • WNUT (ACL) 2021 • Evangelia Spiliopoulou, Tanay Kumar Saha, Joel Tetreault, Alejandro Jaimes
Furthermore, we show that our approach significantly outperforms event detection baselines, highlighting the importance of aggregating information across tweets for our task.
no code implementations • 1 Nov 2023 • Neema Kotonya, Saran Krishnasamy, Joel Tetreault, Alejandro Jaimes
This paper describes and analyzes our participation in the 2023 Eval4NLP shared task, which focuses on assessing the effectiveness of prompt-based techniques to empower Large Language Models to handle the task of quality estimation, particularly in the context of evaluating machine translations and summaries.
1 code implementation • 16 Oct 2023 • Zijian Ding, Alison Smith-Renner, Wenjuan Zhang, Joel R. Tetreault, Alejandro Jaimes
To explore how humans can best leverage LLMs for writing and how interacting with these models affects feelings of ownership and trust in the writing process, we compared common human-AI interaction types (e. g., guiding system, selecting from system outputs, post-editing outputs) in the context of LLM-assisted news headline generation.
1 code implementation • 10 Jul 2023 • Di Lu, Shihao Ran, Joel Tetreault, Alejandro Jaimes
In this paper, we propose QGA-EE, which enables a Question Generation (QG) model to generate questions that incorporate rich contextual information instead of using fixed templates.
1 code implementation • 30 Jun 2023 • Shihao Ran, Di Lu, Joel Tetreault, Aoife Cahill, Alejandro Jaimes
The ability to conduct retrospective analyses of attacks on human rights defenders over time and by location is important for humanitarian organizations to better understand historical or ongoing human rights violations and thus better manage the global impact of such events.
1 code implementation • 20 Dec 2022 • Liang Ma, Shuyang Cao, Robert L. Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes
The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them.
1 code implementation • 25 Oct 2022 • Hossein Rajaby Faghihi, Bashar Alhafni, Ke Zhang, Shihao Ran, Joel Tetreault, Alejandro Jaimes
This paper presents CrisisLTLSum, the largest dataset of local crisis event timelines available to date.
no code implementations • NAACL 2022 • Vivian Lai, Alison Smith-Renner, Ke Zhang, Ruijia Cheng, Wenjuan Zhang, Joel Tetreault, Alejandro Jaimes
Automatic summarization methods are efficient but can suffer from low quality.
no code implementations • NAACL (TextGraphs) 2021 • Sanghamitra Dutta, Liang Ma, Tanay Kumar Saha, Di Lu, Joel Tetreault, Alejandro Jaimes
Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection.
1 code implementation • 23 Jul 2020 • Swati Padhee, Tanay Kumar Saha, Joel Tetreault, Alejandro Jaimes
Social media has quickly grown into an essential tool for people to communicate and express their needs during crisis events.
1 code implementation • CVPR 2020 • Mahdi Abavisani, Liwei Wu, Shengli Hu, Joel Tetreault, Alejandro Jaimes
Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting breaking events around the world in real-time.
no code implementations • 13 Dec 2019 • Chidubem Arachie, Manas Gaur, Sam Anzaroot, William Groves, Ke Zhang, Alejandro Jaimes
Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable.
2 code implementations • 6 Sep 2016 • Yale Song, Miriam Redi, Jordi Vallmitjana, Alejandro Jaimes
Our system selects attractive thumbnails by analyzing various visual quality and aesthetic metrics of video frames, and performs a clustering analysis to determine the relevance to video content, thus making the resulting thumbnails more representative of the video.
Multimedia
1 code implementation • CVPR 2016 • Yuncheng Li, Yale Song, Liangliang Cao, Joel Tetreault, Larry Goldberg, Alejandro Jaimes, Jiebo Luo
The motivation for this work is to develop a testbed for image sequence description systems, where the task is to generate natural language descriptions for animated GIFs or video clips.
no code implementations • LREC 2016 • Dragomir Radev, Amanda Stent, Joel Tetreault, Aasish Pappu, Aikaterini Iliakopoulou, Agustin Chanfreau, Paloma de Juan, Jordi Vallmitjana, Alejandro Jaimes, Rahul Jha, Bob Mankoff
The New Yorker publishes a weekly captionless cartoon.
no code implementations • CVPR 2015 • Wen-Sheng Chu, Yale Song, Alejandro Jaimes
We present video co-summarization, a novel perspective to video summarization that exploits visual co-occurrence across multiple videos.
no code implementations • CVPR 2015 • Yale Song, Jordi Vallmitjana, Amanda Stent, Alejandro Jaimes
We observe that a video title is often carefully chosen to be maximally descriptive of its main topic, and hence images related to the title can serve as a proxy for important visual concepts of the main topic.
no code implementations • 28 Jan 2015 • Miriam Redi, Nikhil Rasiwasia, Gaurav Aggarwal, Alejandro Jaimes
Digital portrait photographs are everywhere, and while the number of face pictures keeps growing, not much work has been done to on automatic portrait beauty assessment.
no code implementations • CVPR 2014 • Miriam Redi, Neil O Hare, Rossano Schifanella, Michele Trevisiol, Alejandro Jaimes
The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content.