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Proposer
Gavin Abercrombie
Title
Learning with Disagreement
Goal
To develop NLP models that take in to account disagreement in human preferences and responses
Description
Traditionally, NLP datasets have consisted of data annotated with single 'gold standard' labels. But in reality, people often disagree on their interpretation of the meanings behind natural language expressions. This project will tackle the problem of how to model datasets that include multiple labels for each item and harness the disagreement information for better classification and generation performance across a range of NLP tasks. There will also be the opportunity to enter the 2024 shared task on Learning with Disagreements (LeWiDi).
Resources
Background
Perspectivist Data Manifesto: https://pdai.info/ Perspectivist Approaches to NLP: https://nlperspectives.di.unito.it/
Url
External Link
Difficulty Level
Moderate
Ethical Approval
Full
Number Of Students
2
Supervisor
Gavin Abercrombie
Keywords
nlp, nlg, classification
Degrees
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Robotics with Industrial Application
Postgraduate Diploma in Artificial Intelligence
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