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The'Problem'of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation
Human variation in labeling is often considered noise. Annotation projects for machine
learning (ML) aim at minimizing human label variation, with the assumption to maximize …
learning (ML) aim at minimizing human label variation, with the assumption to maximize …
Learning from disagreement: A survey
Abstract Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer
evidence that humans disagree, from objective tasks such as part-of-speech tagging to more …
evidence that humans disagree, from objective tasks such as part-of-speech tagging to more …
Inherent disagreements in human textual inferences
We analyze human's disagreements about the validity of natural language inferences. We
show that, very often, disagreements are not dismissible as annotation “noise”, but rather …
show that, very often, disagreements are not dismissible as annotation “noise”, but rather …
Why don't you do it right? analysing annotators' disagreement in subjective tasks
Annotators' disagreement in linguistic data has been recently the focus of multiple initiatives
aimed at raising awareness on issues related to 'majority voting'when aggregating diverging …
aimed at raising awareness on issues related to 'majority voting'when aggregating diverging …
Agreeing to disagree: Annotating offensive language datasets with annotators' disagreement
Since state-of-the-art approaches to offensive language detection rely on supervised
learning, it is crucial to quickly adapt them to the continuously evolving scenario of social …
learning, it is crucial to quickly adapt them to the continuously evolving scenario of social …
What can we learn from collective human opinions on natural language inference data?
Despite the subjective nature of many NLP tasks, most NLU evaluations have focused on
using the majority label with presumably high agreement as the ground truth. Less attention …
using the majority label with presumably high agreement as the ground truth. Less attention …
Consensus and subjectivity of skin tone annotation for ML fairness
Understanding different human attributes and how they affect model behavior may become
a standard need for all model creation and usage, from traditional computer vision tasks to …
a standard need for all model creation and usage, from traditional computer vision tasks to …
Everyone's voice matters: Quantifying annotation disagreement using demographic information
In NLP annotation, it is common to have multiple annotators label the text and then obtain
the ground truth labels based on major annotators' agreement. However, annotators are …
the ground truth labels based on major annotators' agreement. However, annotators are …
Conformalized credal set predictors
Credal sets are sets of probability distributions that are considered as candidates for an
imprecisely known ground-truth distribution. In machine learning, they have recently …
imprecisely known ground-truth distribution. In machine learning, they have recently …
[PDF][PDF] Learning part-of-speech taggers with inter-annotator agreement loss
In natural language processing (NLP) annotation projects, we use inter-annotator
agreement measures and annotation guidelines to ensure consistent annotations. However …
agreement measures and annotation guidelines to ensure consistent annotations. However …