Capturing Ambiguity in Crowdsourcing Frame Disambiguation
This addresses the need for better handling of ambiguity in semantic annotation tasks for computational linguists, though it is incremental as it builds on existing crowdsourcing methods.
The paper tackled the problem of frame disambiguation in computational linguistics by using crowdsourcing with multiple workers per sentence to capture inter-annotator disagreement, achieving an F1 score greater than 0.67 compared to expert annotations and highlighting cases of ambiguity that challenge single truth values.
FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. In this paper, we present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. We perform an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, and show that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. We highlight cases where the crowd annotation was correct even though the expert is in disagreement, arguing for the need to have multiple annotators per sentence. Most importantly, we examine cases in which crowd workers could not agree, and demonstrate that these cases exhibit ambiguity, either in the sentence, frame, or the task itself, and argue that collapsing such cases to a single, discrete truth value (i.e. correct or incorrect) is inappropriate, creating arbitrary targets for machine learning.