Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue

Jeiyoon Park, Yoonna Jang, Chanhee Lee, Heuiseok Lim

Abstract
The focus of this work is to investigate unsupervised approaches to overcome quintessential challenges in designing task-oriented dialog schema: assigning intent labels to each dialog turn (intent clustering) and generating a set of intents based on the intent clustering methods (intent induction). We postulate there are two salient factors for automatic induction of intents: (1) clustering algorithm for intent labeling and (2) user utterance embedding space. We compare existing off-the-shelf clustering models and embeddings based on DSTC11 evaluation. Our extensive experiments demonstrate that the combined selection of utterance embedding and clustering method in the intent induction task should be carefully considered. We also present that pretrained MiniLM with Agglomerative clustering shows significant improvement in NMI, ARI, F1, accuracy and example coverage in intent induction tasks. The source codes are available at https://github.com/Jeiyoon/dstc11-track2.
Anthology ID:
2023.dstc-1.8
Volume:
Proceedings of The Eleventh Dialog System Technology Challenge
Month:
September
Year:
2023
Address:
Prague, Czech Republic
Editors:
Yun-Nung Chen, Paul Crook, Michel Galley, Sarik Ghazarian, Chulaka Gunasekara, Raghav Gupta, Behnam Hedayatnia, Satwik Kottur, Seungwhan Moon, Chen Zhang
Venues:
DSTC | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
57–66
Language:
URL:
https://aclanthology.org/2023.dstc-1.8
DOI:
Bibkey:
Cite (ACL):
Jeiyoon Park, Yoonna Jang, Chanhee Lee, and Heuiseok Lim. 2023. Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue. In Proceedings of The Eleventh Dialog System Technology Challenge, pages 57–66, Prague, Czech Republic. Association for Computational Linguistics.
Cite (Informal):
Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue (Park et al., DSTC-WS 2023)
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