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Disfluency Detection using Auto Correlational Neural Networks

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Figure 1: Cosine similarity between word embeddingvectors learned by the ACNN model for the sentence “Iknow they use that I mean they sell those” (with disflu-ent words highlighted)
Figure 2: ACNN overview for labeling the target word “boston”. A patch of words is fed into an auto-correlationallayer
Figure 3: Distribution over the number of words inbetween the reparandum and the interregnum in theSwitchboard training set (indicating the distance be-tween “rough copies”).
Table 3: F-scores for different types of disfluencies ona subset of the Switchboard dev set containing 140 dis-fluent structures — including 85 repetitions (Rep.), 51corrections (Cor.) and 4 restarts (Res.).

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