Abstract
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.
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Acknowledgement
This work was supported by the National Key R &D Program of China under Grant No. 2020AAA0108600 and Guizhou Province Science and Technology Plan Project-Research on Knowledge Management Technology Based on KG.
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Pang, J., Xu, H., Song, S., Zou, B., He, X. (2023). MFDG: A Multi-Factor Dialogue Graph Model for Dialogue Intent Classification. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13714. Springer, Cham. https://doi.org/10.1007/978-3-031-26390-3_40
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