We propose a component that gets a request and a correction and outputs a corrected request. To get this corrected request, the entities in the correction phrase replace their corresponding entities in the request. In addition, the proposed component outputs these pairs of corresponding reparandum and repair entity. These entity pairs can be used, for example, for learning in a life-long learning component of a dialog system to reduce the need for correction in future dialogs. For the approach described in this work, we fine-tune BERT for sequence labeling. We created a dataset to evaluate our component; for which we got an accuracy of 93.28 %. An accuracy of 88.58 % has been achieved for out-of-domain data. This accuracy shows that the proposed component is learning the concept of corrections and can be developed to be used as an upstream component to avoid the need for collecting data for request corrections for every new domain.
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