arXiv Open Access 2022

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

Xue-Yong Fu Cheng Chen Md Tahmid Rahman Laskar Shashi Bhushan TN Simon Corston-Oliver

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Abstrak

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.

Topik & Kata Kunci

cs.CL

Penulis (5)

Xue-Yong Fu

Cheng Chen

Md Tahmid Rahman Laskar

Shashi Bhushan TN

Simon Corston-Oliver

Format Sitasi

APA MLA BibTeX

Fu, X., Chen, C., Laskar, M.T.R., TN, S.B., Corston-Oliver, S. (2022). An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts. https://arxiv.org/abs/2209.13736

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Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓