| Challenge: | Existing work assumes that dialog intents are expressed in query utterances and captured in the rest of the dialog. |
| Approach: | They propose a dialog intent induction task and propose alternating-view k-means for clustering . they split a conversation into two independent views and exploit multi-view clustering techniques . |
| Outcome: | The proposed approach can induce better dialog intent clusters than state-of-the-art clustering methods. |
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Supervised Clustering of Questions into Intents for Dialog System Applications (D18-1)
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| Challenge: | Existing methods for detecting intents in text are task-specific and costly . current methods focus on manually analyzing user questions and creating a taxonomy of intents to be attached to the appropriate actions. |
| Approach: | They propose a model for automatically clustering questions into user intents to help design tasks . they use powerful semantic classifiers and supervised clustering methods based on structured output . |
| Outcome: | The proposed model improves on two intent clustering corpora on two languages/domains. |
Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining (2022.naacl-industry)
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| Challenge: | Existing approaches to clustering unlabeled utterances are based on transformerbased sentence embedding methods. |
| Approach: | They propose a semantic embedding approach that can be leveraged to identify clusters of utterances that correspond to unhandled intents. |
| Outcome: | The proposed approach can identify clusters of utterances that correspond to unhandled intents from a large collection of enterprise virtual assistant data using a multi-task softmax loss. |
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering (2022.naacl-main)
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| Challenge: | Existing approaches to intent detection rely on epoch wise clustering and classification based on labeled and unlabeled data. |
| Approach: | They propose an end-to-end deep contrastive clustering algorithm that jointly updates model parameters and cluster centers via supervised and self-supervised learning. |
| Outcome: | The proposed approach outperforms baselines on five public datasets and human-in-the-loop variant for practical deployment. |
ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery (2023.findings-emnlp)
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| Challenge: | Existing methods for identifying new intent categories focus on relations between utterances and clusters, while neglecting the usage of semantics. |
| Approach: | They propose a method that leverages contrastive learning and label semantic alignment to learn meaningful representations of intent clusters. |
| Outcome: | The proposed method outperforms existing methods and suggests meaningful intent labels. |
Dialog Intent Structure: A Hierarchical Schema of Linked Dialog Acts (L18-1)
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| Challenge: | a schema for dialog representation captures the pragmatic intents of the conversation independently from any semantic representation. |
| Approach: | They propose a hierarchical and extensible schema for dialog representation . schema captures pragmatic intents of conversation independently from any semantic representation based on semantic content . |
| Outcome: | The proposed schema captures the pragmatic intents of the conversation independently from any semantic representation. |
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems (2020.coling-industry)
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| Challenge: | a number of dialog systems have been developed to perform tasks with high accuracy on benchmarks, but there is a problem with annotated seed data. |
| Approach: | They propose a model that augments initial seed data by paraphrasing existing utterances automatically. |
| Outcome: | The proposed approach improves intent classification and slot labeling on a public dataset and with a real-world dialog system. |
Intent Features for Rich Natural Language Understanding (2021.naacl-industry)
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| Challenge: | generic dialog systems, or chatbots, are increasingly popular, but most industrial dialog systems are built for specific clients and use cases. |
| Approach: | They propose a new neural network architecture that allows for domain and topic agnostic properties of intents that can be learnt from syntactic cues only. |
| Outcome: | The proposed model improves on baselines for identifying intent features in a deployed, multi-intent natural language understanding module. |
Intent Mining from past conversations for Conversational Agent (2020.coling-main)
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| Challenge: | Conversational systems are of primary interest in the AI community . many commercial bot building frameworks require a collection of user utterances and corresponding intent to train an intent model. |
| Approach: | They propose an intent discovery framework that can mine a vast amount of conversational logs and generate labeled data sets for training intent models. |
| Outcome: | The proposed framework can mine conversational logs and generate labeled data sets for training intent models. |
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection (2022.findings-emnlp)
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Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao, Zhifang Sui
| Challenge: | DialogUSR is a plug-in and domain-agnostic module that empowers multi-intent detection for chatbots . a single user query triggers inquiries on highspeed train ticket price and weather of destination. |
| Approach: | They propose a dialog utterance splitting and reformulation task that splits multi-intent user query into multiple single-intention sub-queries and recovers all coreferred and omitted information in the sub-questions. |
| Outcome: | The proposed model can be used to split multi-intent user queries into multiple sub-queries . it can be trained in two stages and perform in-depth analyses on the proposed models . |
Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues (2025.emnlp-main)
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| Challenge: | Existing intent clustering methods rely on embedding distance metrics and neglect of underlying semantic structures. |
| Approach: | They propose an LLM-in-the-loop framework that integrates language understanding capabilities into conventional clustering algorithms. |
| Outcome: | The proposed framework outperforms baselines in Chinese and improves quality, cost efficiency and downstream applications. |