Aggregating and Predicting Sequence Labels from Crowd Annotations

ACL 2017 An Thanh NguyenByron WallaceJunyi Jessy LiAni NenkovaMatthew Lease

Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text... (read more)

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