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Found 10 papers, 0 papers with code
On the Value of ML Models
We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics that better capture the value delivered by their model in practical applications.
The Science of Rejection: A Research Area for Human Computation
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention
Recent studies in fair Representation Learning have observed a strong inclination for natural language processing (NLP) models to exhibit discriminatory stereotypes across gender, religion, race and many such social constructs.
Crowdsourcing Diverse Paraphrases for Training Task-oriented Bots
A prominent approach to build datasets for training task-oriented bots is crowd-based paraphrasing.
Active Hybrid Classification
Hybrid crowd-machine classifiers can achieve superior performance by combining the cost-effectiveness of automatic classification with the accuracy of human judgment.
Chatbots as conversational healthcare services
Chatbots are emerging as a promising platform for accessing and delivering healthcare services.
Siamese Graph Neural Networks for Data Integration
In this work, we propose a general approach to modeling and integrating entities from structured data, such as relational databases, as well as unstructured sources, such as free text from news articles.
Combining Crowd and Machines for Multi-predicate Item Screening
This paper discusses how crowd and machine classifiers can be efficiently combined to screen items that satisfy a set of predicates.
Crowdsourcing for Reminiscence Chatbot Design
In this work-in-progress paper we discuss the challenges in identifying effective and scalable crowd-based strategies for designing content, conversation logic, and meaningful metrics for a reminiscence chatbot targeted at older adults.
Crowd-Machine Collaboration for Item Screening
In this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates.
