Error correction and extraction in request dialogs

• URL: http://arxiv.org/abs/2004.04243v4
• Date: Tue, 20 Jun 2023 17:58:11 GMT
• Title: Error correction and extraction in request dialogs
• Authors: Stefan Constantin and Alex Waibel
• Abstract summary: Component gets the last two utterances of a user and can detect whether the last utterance is an error correction of the second last utterance. It corrects the second last utterance according to the error correction in the last utterance and outputs the extracted pairs of reparandum and repair entity. One error correction detection and one error correction approach can be combined to a pipeline or the error correction approaches can be trained and used end-to-end to avoid two components.
• Score: 12.137183622356197
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a dialog system utility component that gets the last two utterances of a user and can detect whether the last utterance is an error correction of the second last utterance. If yes, it corrects the second last utterance according to the error correction in the last utterance and outputs the extracted pairs of reparandum and repair entity. This component offers two advantages, learning the concept of corrections to avoid collecting corrections for every new domain and extracting reparandum and repair pairs, which offers the possibility to learn out of it. For the error correction one sequence labeling and two sequence to sequence approaches are presented. For the error correction detection these three error correction approaches can also be used and in addition, we present a sequence classification approach. One error correction detection and one error correction approach can be combined to a pipeline or the error correction approaches can be trained and used end-to-end to avoid two components. We modified the EPIC-KITCHENS-100 dataset to evaluate the approaches for correcting entity phrases in request dialogs. For error correction detection and correction, we got an accuracy of 96.40 % on synthetic validation data and an accuracy of 77.81 % on human-created real-world test data.

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