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Call Reason Prediction using Hierarchical Models

Published: 07 June 2023 Publication History

Abstract

We propose a supervised method to predict a reason for making call to customer contact centers by exploiting domain knowledge wherein domain experts propose a few reason codes to label the calls. They also mark an utterance in the call that would indicate the reason for making the call, this is called a marked utterance. Thus we solve a two-level prediction problem for a call, first to classify whether an utterance is marked, and then predict a reason code around this marked utterance as a label for the call. We use hierarchical models (LSTM or BERT layered on top of a transformer) for modeling purpose. We carry out comprehensive experiments using our internal (labeled) call data set, marked by domain experts, and achieve some interesting results like the accuracy of prediction of reason codes leaps to 85% using hierarchical model.

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cover image ACM Conferences
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
March 2023
1932 pages
ISBN:9781450395175
DOI:10.1145/3555776
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Publication History

Published: 07 June 2023

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Author Tags

  1. call transcripts
  2. utterances
  3. reason codes
  4. hierarchical transformer
  5. LSTM
  6. attention
  7. two-level prediction

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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