Abstract
Multi-domain spoken dialog system should be able to detect more than one domain from a user’s utterance. However, it is difficult to train an accurate binary classifier of a domain based on only positive and unlabeled examples. This paper improves hierarchical clustering algorithm to automatically identify reliable negative examples among unlabeled examples. This paper also verifies three linkage criteria that measure the distance between two clusters. In experiments, the proposed method resulted in the highest gain of F 1 score compared to the existing methods.
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Acknowledgments
This work was supported by ICT R&D program of MSIP/IITP [14-824-09-014, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)]. This work was supported by National Research Foundation of Korean (NRF) [NRF-2014R1A2A1A01003041, Development of Multi-party Anticipatory Knowledge-Intensive Natural Language Dialog System].
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Ryu, S., Song, J., Koo, S., Kwon, S., Lee, G.G. (2015). Detecting Multiple Domains from User’s Utterance in Spoken Dialog System. In: Lee, G., Kim, H., Jeong, M., Kim, JH. (eds) Natural Language Dialog Systems and Intelligent Assistants. Springer, Cham. https://doi.org/10.1007/978-3-319-19291-8_10
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DOI: https://doi.org/10.1007/978-3-319-19291-8_10
Publisher Name: Springer, Cham
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