Search Results for author:
Found 14 papers, 5 papers with code
Learning to Rank Visual Stories From Human Ranking Data
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.
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling
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
The Use of Generative Search Engines for Knowledge Work and Complex Tasks
Until recently, search engines were the predominant method for people to access online information.
OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning
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.
Pragmatic Radiology Report Generation
When pneumonia is not found on a chest X-ray, should the report describe this negative observation or omit it?
Learning Human-Compatible Representations for Case-Based Decision Support
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.
Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems
Specifically, we propose an approach that performs contextual dynamic prompting where the prompts are learnt from dialog contexts.
Selective Explanations: Leveraging Human Input to Align Explainable AI
As a result, current XAI techniques are often found to be hard to use and lack effectiveness.
Machine Explanations and Human Understanding
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.
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
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.
Learning to Simulate on Sparse Trajectory Data
Simulation of the real-world traffic can be used to help validate the transportation policies.
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
However, existing uncertainty estimation approaches often failed in handling high-dimensional data, which are present in multi-sourced data.
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
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
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
