Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer
This addresses the problem of adapting dialogue systems to real-world scenarios by handling multi-domain data and clustering intents, though it appears incremental as it builds on existing embedding and clustering techniques.
The paper tackles the challenges of detecting and inducing new intents in Task-Oriented Dialogue systems by proposing a semantic multi-view model with Multi Domain Batch and Proxy Gradient Transfer, which significantly improves Open Intent Induction performance compared to baselines.
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.