Improving Dialogue Intent Classification with a Knowledge-Enhanced Multifactor Graph Model (Student Abstract)
Huinan Xu, Jinhui Pang, Shuangyong Song, Bo Zou
Abstract
Although current Graph Neural Network (GNN) based models achieved good performances in Dialogue Intent Classification (DIC), they leaf the inherent domain-specific knowledge out of consideration, leading to the lack of ability of acquiring fine-grained semantic information. In this paper, we propose a Knowledge-Enhanced Multifactor Graph (KEMG) Model for DIC. We firstly present a knowledge-aware utterance encoder with the help of a domain-specific knowledge graph, fusing token-level and entity-level semantic information, then design a heterogeneous dialogue graph encoder by explicitly modeling several factors that matter to contextual modeling of dialogues. Experiment results show that our proposed method outperforms other GNN-based methods on a dataset collected from a real-world online customer service dialogue system on the e-commerce website, JD.
BibTeX
@article{Xu_Pang_Song_Zou_2024, title={Improving Dialogue Intent Classification with a Knowledge-Enhanced Multifactor Graph Model (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27043}, DOI={10.1609/aaai.v37i13.27043}, abstractNote={Although current Graph Neural Network (GNN) based models achieved good performances in Dialogue Intent Classification (DIC), they leaf the inherent domain-specific knowledge out of consideration, leading to the lack of ability of acquiring fine-grained semantic information. In this paper, we propose a Knowledge-Enhanced Multifactor Graph (KEMG) Model for DIC. We firstly present a knowledge-aware utterance encoder with the help of a domain-specific knowledge graph, fusing token-level and entity-level semantic information, then design a heterogeneous dialogue graph encoder by explicitly modeling several factors that matter to contextual modeling of dialogues. Experiment results show that our proposed method outperforms other GNN-based methods on a dataset collected from a real-world online customer service dialogue system on the e-commerce website, JD.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Xu, Huinan and Pang, Jinhui and Song, Shuangyong and Zou, Bo}, year={2024}, month={Jul.}, pages={16366-16367} }