Quoref is a 24K-question coreference-focused reading comprehension benchmark on which the top model reaches 70.5 F1, well below the 93.4 F1 human estimate.
Model-based annotation of coreference
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Humans do not make inferences over texts, but over models of what texts are about. When annotators are asked to annotate coreferent spans of text, it is therefore a somewhat unnatural task. This paper presents an alternative in which we preprocess documents, linking entities to a knowledge base, and turn the coreference annotation task -- in our case limited to pronouns -- into an annotation task where annotators are asked to assign pronouns to entities. Model-based annotation is shown to lead to faster annotation and higher inter-annotator agreement, and we argue that it also opens up for an alternative approach to coreference resolution. We present two new coreference benchmark datasets, for English Wikipedia and English teacher-student dialogues, and evaluate state-of-the-art coreference resolvers on them.
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cs.CL 1years
2019 1verdicts
ACCEPT 1representative citing papers
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Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
Quoref is a 24K-question coreference-focused reading comprehension benchmark on which the top model reaches 70.5 F1, well below the 93.4 F1 human estimate.