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Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer

arXiv 2303.13099 ↗First posted 23 Mar 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world. In this paper, we suggest the semantic multi-view model to resolve these two challenges: (1) SBERT for General Embedding (GE), (2) Multi Domain Batch (MDB) for dialogue domain knowledge, and (3) Proxy Gradient Transfer (PGT) for cluster-specialized semantic. MDB feeds diverse dialogue datasets to the model at once to tackle the multi-domain problem by learning the multiple domain knowledge. We introduce a novel method PGT, which employs the Siamese network to fine-tune the model with a clustering method directly.Our model can learn how to cluster dialogue utterances by using PGT. Experimental results demonstrate that our multi-view model with MDB and PGT significantly improves the Open Intent Induction performance compared to baseline systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 23 Mar 2023.

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26 of 42 references.

References · 26

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14 more
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  14. 2020Cross-lingual Transfer Learning for COVID-19 Outbreak Alignmentcited by 3

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