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Found 6 papers, 1 papers with code
A Cluster Ranking Model for Full Anaphora Resolution
In this paper, we introduce an architecture to simultaneously identify non-referring expressions (including expletives, predicative s, and other types) and build coreference chains, including singletons.
Ranked #1 on
Coreference Resolution
on The ARRAU Corpus
Beyond Black \& White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning
Supervised learning assumes that a ground truth label exists.
SemEval-2021 Task 12: Learning with Disagreements
Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision.
We Need to Consider Disagreement in Evaluation
Instead, we suggest that we need to better capture the sources of disagreement to improve today’s evaluation practice.
Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa
Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models.
SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)
We report on the second LeWiDi shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements-as training with aggregated labels for subjective NLP tasks is a particularly obvious misrepresentation of the data; and (iii) for the evaluation, we concentrate on soft approaches to evaluation.
