1 code implementation • ACL 2022 • Chi-Yang Hsu, Yun-Wei Chu, Vincent Chen, Kuan-Chieh Lo, Chacha Chen, Ting-Hao Huang, Lun-Wei Ku
In this paper, we present the VHED (VIST Human Evaluation Data) dataset, which first re-purposes human evaluation results for automatic evaluation; hence we develop Vrank (VIST Ranker), a novel reference-free VIST metric for story evaluation.
no code implementations • EMNLP (WNUT) 2020 • Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter Language Modelling +4
no code implementations • 19 Mar 2024 • Siddharth Suri, Scott Counts, Leijie Wang, Chacha Chen, Mengting Wan, Tara Safavi, Jennifer Neville, Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Sathish Manivannan, Nagu Rangan, Longqi Yang
Until recently, search engines were the predominant method for people to access online information.
no code implementations • 20 Feb 2024 • Jiaqi Ma, Vivian Lai, Yiming Zhang, Chacha Chen, Paul Hamilton, Davor Ljubenkov, Himabindu Lakkaraju, Chenhao Tan
However, properly evaluating the effectiveness of the XAI methods inevitably requires the involvement of human subjects, and conducting human-centered benchmarks is challenging in a number of ways: designing and implementing user studies is complex; numerous design choices in the design space of user study lead to problems of reproducibility; and running user studies can be challenging and even daunting for machine learning researchers.
1 code implementation • 28 Nov 2023 • Dang Nguyen, Chacha Chen, He He, Chenhao Tan
When pneumonia is not found on a chest X-ray, should the report describe this negative observation or omit it?
1 code implementation • 6 Mar 2023 • Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan
Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans.
no code implementations • 30 Jan 2023 • Sandesh Swamy, Narges Tabari, Chacha Chen, Rashmi Gangadharaiah
Specifically, we propose an approach that performs contextual dynamic prompting where the prompts are learnt from dialog contexts.
no code implementations • 23 Jan 2023 • Vivian Lai, Yiming Zhang, Chacha Chen, Q. Vera Liao, Chenhao Tan
As a result, current XAI techniques are often found to be hard to use and lack effectiveness.
1 code implementation • 8 Feb 2022 • Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan
Our key result is that without assumptions about task-specific intuitions, explanations may potentially improve human understanding of model decision boundary, but they cannot improve human understanding of task decision boundary or model error.
no code implementations • 21 Dec 2021 • Vivian Lai, Chacha Chen, Q. Vera Liao, Alison Smith-Renner, Chenhao Tan
Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions.
no code implementations • 22 Mar 2021 • Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, Zhenhui Li
Simulation of the real-world traffic can be used to help validate the transportation policies.
no code implementations • 22 Oct 2020 • Chacha Chen, Junjie Liang, Fenglong Ma, Lucas M. Glass, Jimeng Sun, Cao Xiao
However, existing uncertainty estimation approaches often failed in handling high-dimensional data, which are present in multi-sourced data.
no code implementations • 29 Sep 2020 • Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter Language Modelling +5
4 code implementations • 11 May 2019 • Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Wei-Nan Zhang, Yanmin Zhu, Kai Xu, Zhenhui Li
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.