BenchmarkSemEval 2016
SemEval-2016
A natural language processing evaluation benchmark introduced at the SemEval 2016 competition, encompassing multiple tasks including stance detection. It is a widely used standard evaluation dataset in the NLP research community.
Timeline (last 90 days)
Oct 12
On the SemEval-2016 dataset, class-conditioned feedback achieved higher average Macro-F1 scores than aggregated feedback on four out of five backbone models.
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