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Preference-Learning with Qualitative Agreement for Sentence Level Emotional AnnotationsResearch Paper • August 19th, 2021
The perceptual evaluation of emotional attributes is noisy due to inconsistencies between annotators. The low inter-evaluator agreement arises due to the complex nature of emotions. Con- ventional approaches average scores provided by multiple an- notators. While this approach reduces the influence of dissi- dent annotations, previous studies have showed the value of considering individual evaluations to better capture the under- lying ground-truth. One of these approaches is the qualitative agreement (QA) method, which provides an alternative frame- work that captures the inherent trends amongst the annotators. While previous studies have focused on using the QA method for time-continuous annotations from a fixed number of anno- tators, most emotional databases are annotated with attributes at the sentence-level (e.g., one global score per sentence). This study proposes a novel formulation based on the QA framework to estimate reliable sentence-level annotations for preference- learni
Preference-Learning with Qualitative Agreement for Sentence Level Emotional AnnotationsResearch Paper • August 26th, 2018
The perceptual evaluation of emotional attributes is noisy due to inconsistencies between annotators. The low inter-evaluator agreement arises due to the complex nature of emotions. Con- ventional approaches average scores provided by multiple an- notators. While this approach reduces the influence of dissi- dent annotations, previous studies have showed the value of considering individual evaluations to better capture the under- lying ground-truth. One of these approaches is the qualitative agreement (QA) method, which provides an alternative frame- work that captures the inherent trends amongst the annotators. While previous studies have focused on using the QA method for time-continuous annotations from a fixed number of anno- tators, most emotional databases are annotated with attributes at the sentence-level (e.g., one global score per sentence). This study proposes a novel formulation based on the QA framework to estimate reliable sentence-level annotations for preference- learni
