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

• URL: http://arxiv.org/abs/2303.13099v3
• Date: Sun, 13 Aug 2023 15:06:43 GMT
• Title: Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer
• Authors: Hyukhun Koh, Haesung Pyun, Nakyeong Yang, Kyomin Jung
• Abstract summary: In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world. We suggest the semantic multi-view model to resolve these two challenges. We introduce a novel method PGT, which employs the Siamese network to fine-tune the model with a clustering method directly.
• Score: 16.804434185847363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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