BERTicsson: A Recommender System For Troubleshooting

Troubleshooting in the telecommunication industry is a time-consuming task, often involving text understanding, which is challenging to automate due to its domain/company-specific features. This work aims to build a model to retrieve solutions for newly reported problems in automated and quick ways. To this end, we present BERTicsson, a BERT-based model that uses two main stages for (i) retrieving a shortlist of candidate answers for new problems and (ii) raking them accordingly. We study the performance of BERTicsson using Ericsson’s troubleshooting dataset and show that it significantly improves the accuracy of the recommended answers compared to non-BERT models, such as BM25.

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