TY - GEN
T1 - Improving Dialogue Intent Classification with a Knowledge-Enhanced Multifactor Graph Model
AU - Xu, Huinan
AU - Pang, Jinhui
AU - Song, Shuangyong
AU - Zou, Bo
N1 - Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/6/27
Y1 - 2023/6/27
N2 - Although current Graph Neural Network (GNN) based models achieved good performances in Dialogue Intent Classification (DIC), they leave 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.
AB - Although current Graph Neural Network (GNN) based models achieved good performances in Dialogue Intent Classification (DIC), they leave 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.
UR - https://www.scopus.com/pages/publications/85168242533
U2 - 10.1609/aaai.v37i13.27043
DO - 10.1609/aaai.v37i13.27043
M3 - Conference contribution
AN - SCOPUS:85168242533
T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
SP - 16366
EP - 16367
BT - AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations
A2 - Williams, Brian
A2 - Chen, Yiling
A2 - Neville, Jennifer
PB - AAAI press
T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Y2 - 7 February 2023 through 14 February 2023
ER -