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We propose a dialog system utility component that gets the last twoutterances of a user and can detect whether the last utterance is an errorcorrection of the second last utterance. If yes, it corrects the second lastutterance according to the error correction in the last utterance and outputsthe extracted pairs of reparandum and repair entity. This component offers twoadvantages, learning the concept of corrections to avoid collecting correctionsfor every new domain and extracting reparandum and repair pairs, which offersthe possibility to learn out of it.  For the error correction one sequence labeling and two sequence to sequenceapproaches are presented. For the error correction detection these three errorcorrection approaches can also be used and in addition, we present a sequenceclassification approach. One error correction detection and one errorcorrection approach can be combined to a pipeline or the error correctionapproaches can be trained and used end-to-end to avoid two components. Wemodified the EPIC-KITCHENS-100 dataset to evaluate the approaches forcorrecting entity phrases in request dialogs. For error correction detectionand correction, we got an accuracy of 96.40 % on synthetic validation data andan accuracy of 77.81 % on human-created real-world test data.

Error correction and extraction in request dialogs