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

本研究提出了一种有效的方法，使用有噪声的商务电话对话转录本进行训练的命名实体识别 (NER) 模型，使用 LUKE 作为教师模型，使用大量弱标记数据和少量人工标注数据教授一个 smaller DistilBERT-based 学生模型，并在满足商业电话产品实际约束条件的情况下实现高准确性和即时性能