icsPLMs: Exploring Pre-trained Language Models in Intelligent Customer Service (Student Abstract)

Authors

  • Shixuan Liu Australian National University
  • Chao Wang China Telecom Corporation Ltd. Data&AI Technology Company
  • Shuangyong Song China Telecom Corporation Ltd. Data&AI Technology Company

DOI:

https://doi.org/10.1609/aaai.v38i21.30475

Keywords:

Pre-trained Language Models, Intent Classification, Query Matching

Abstract

Pre-trained language models have shown their high performance of text processing in intelligent customer service platforms. However, these models do not leverage domain specific information. In this paper, we propose icsPLMs optimized for intelligent customer service on both word and sentence levels. Our experimental results represent that using targeted strategies can further improve the performance of pre-trained language models in this field.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Liu, S., Wang, C., & Song, S. (2024). icsPLMs: Exploring Pre-trained Language Models in Intelligent Customer Service (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23565-23566. https://doi.org/10.1609/aaai.v38i21.30475

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program