These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Metrics concerned:Accuracy
Previous attempts to incorporate a mention detection step into end-to-end
neural coreference resolution for English have been hampered by the lack of
singleton mention span data as well as other entity information. This paper
presents a coreference model that learns singletons as well as features such as
entity type and information status via a multi-task learning-based approach.
This approach achieves new state-of-the-art scores on the OntoGUM benchmark
(+2.7 points) and increases robustness on multiple out-of-domain datasets (+2.3
points on average), likely due to greater generalizability for mention
detection and utilization of more data from singletons when compared to only
coreferent mention pair matching.
About the metric use case
You can click on the links to see the associated metric use cases
Objective(s):
Purpose(s):
Target sector(s):
