TY - GEN
T1 - MFDG
T2 - 22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022
AU - Pang, Jinhui
AU - Xu, Huinan
AU - Song, Shuangyong
AU - Zou, Bo
AU - He, Xiaodong
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Interest in speaker intent classification has been increasing in multi-turn dialogues, as the intention of a speaker is one of the components for dialogue understanding. While most existing methods perform speaker intent classification at utterance-level, the dialogue-level comprehension is ignored. To obtain a full understanding of dialogues, we propose a Multi-Factor Dialogue Graph Model (MFDG) for Dialogue Core Intent (DCI) classification. The model gains an understanding of the entire dialogue by explicitly modeling multi factors that are essential for speaker-specific and contextual information extraction across the dialogue. The main module of MFDG is a heterogeneous graph encoder, where speakers, local discourses, and utterances are modelled in a graph interaction manner. Based on the framework of MFDG, we propose two variants, MFDG-EN and MFDG-EE, to fuse domain knowledge into the dialogue graph. We apply MFDG and its two variants to a real-world online customer service dialogue system on the e-commerce website, JD, in which the MFDG can help achieving an automatic intent-oriented classification of finished service dialogues, and the MFDG-EE can further promote dialogue comprehension with a well-designed knowledge graph. Experiments on this in-house JD dataset and a public DailyDialog dataset demonstrate that MFDG performs reasonably well in multi-turn dialogue classification.
AB - Interest in speaker intent classification has been increasing in multi-turn dialogues, as the intention of a speaker is one of the components for dialogue understanding. While most existing methods perform speaker intent classification at utterance-level, the dialogue-level comprehension is ignored. To obtain a full understanding of dialogues, we propose a Multi-Factor Dialogue Graph Model (MFDG) for Dialogue Core Intent (DCI) classification. The model gains an understanding of the entire dialogue by explicitly modeling multi factors that are essential for speaker-specific and contextual information extraction across the dialogue. The main module of MFDG is a heterogeneous graph encoder, where speakers, local discourses, and utterances are modelled in a graph interaction manner. Based on the framework of MFDG, we propose two variants, MFDG-EN and MFDG-EE, to fuse domain knowledge into the dialogue graph. We apply MFDG and its two variants to a real-world online customer service dialogue system on the e-commerce website, JD, in which the MFDG can help achieving an automatic intent-oriented classification of finished service dialogues, and the MFDG-EE can further promote dialogue comprehension with a well-designed knowledge graph. Experiments on this in-house JD dataset and a public DailyDialog dataset demonstrate that MFDG performs reasonably well in multi-turn dialogue classification.
KW - Core intent classification
KW - Dialogue classification
KW - Graph neural network
UR - http://www.scopus.com/inward/record.url?scp=85151049801&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-26390-3_40
DO - 10.1007/978-3-031-26390-3_40
M3 - Conference contribution
AN - SCOPUS:85151049801
SN - 9783031263897
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 691
EP - 706
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings
A2 - Amini, Massih-Reza
A2 - Canu, Stéphane
A2 - Fischer, Asja
A2 - Guns, Tias
A2 - Kralj Novak, Petra
A2 - Tsoumakas, Grigorios
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 September 2022 through 23 September 2022
ER -