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We present a simple yet effective method to train a named entity recognition(NER) model that operates on business telephone conversation transcripts thatcontain noise due to the nature of spoken conversation and artifacts ofautomatic speech recognition. We first fine-tune LUKE, a state-of-the-art NamedEntity Recognition (NER) model, on a limited amount of transcripts, then use itas the teacher model to teach a smaller DistilBERT-based student model using alarge amount of weakly labeled data and a small amount of human-annotated data.The model achieves high accuracy while also satisfying the practicalconstraints for inclusion in a commercial telephony product: realtimeperformance when deployed on cost-effective CPUs rather than GPUs.

An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts