Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues
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Abstract (arXiv)
State of the art models in intent induction require annotated datasets. However, annotating dialogues is time-consuming, laborious and expensive. In this work, we propose a completely unsupervised framework for intent induction within a dialogue. In addition, we show how pre-processing the dialogue corpora can improve results. Finally, we show how to extract the dialogue flows of intentions by investigating the most common sequences. Although we test our work in the MultiWOZ dataset, the fact that this framework requires no prior knowledge make it applicable to any possible use case, making it very relevant to real world customer support applications across industry.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 28 Jul 2023.
Builds on
10 of 23 references.
References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2018Deep contextualized word representationscited by 2,285
- 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
- 2017Accelerated Hierarchical Density Clusteringcited by 100
- 2020Intent Mining from past conversations for conversational agentcited by 25
- 2019Dialog Intent Induction with Deep Multi-View Clusteringcited by 20
- 2021News Headline Grouping as a Challenging NLU Taskcited by 10
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