CoCoID: Learning Contrastive Representations and Compact Clusters for Semi-Supervised Intent Discovery (2022.emnlp-industry)
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| Challenge: | Existing approaches to intent discovery cluster novel intents with prior knowledge from intent-labeled data in a semi-supervised way. |
| Approach: | They propose a semi-supervised intent discovery framework CoCoID with two components . they propose to discriminate user utterance representation learning and intra-cluster knowledge distillation . |
| Outcome: | The proposed framework outperforms state-of-the-art intent discovery models by over 1.4 ACC and ARI points and 1.1 NMI points across four datasets. |
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| Challenge: | Existing methods for identifying intents from unlabeled utterances are label-intensive, inefficient, and inaccurate. |
| Approach: | They propose a multi-task strategy to leverage unlabeled data and external labeled data for representation learning. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three intent recognition benchmarks. |
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. |
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery (2023.findings-emnlp)
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| Challenge: | Intent-related tasks are typically modeled as separate tasks, but a unified approach is proposed . INTENDD uses an entirely unsupervised contrastive learning strategy for representation learning . |
| Approach: | They propose a unified approach to identifying intents from dialogue utterances . they propose an unsupervised contrastive learning strategy for representation learning . |
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Learning Geometry-Aware Representations for New Intent Discovery (2024.acl-long)
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| Challenge: | Existing methods for intent classification fail to distinguish new intents due to intertwined centers . a novel framework that learns geometry-aware representations to maximally separate all intents is proposed . |
| Approach: | They propose a new intent discovery framework that learns geometry-aware representations to maximally separate all intents. |
| Outcome: | The proposed framework achieves a new state-of-the-art performance on three benchmarking datasets. |
New Intent Discovery with Attracting and Dispersing Prototype (2024.lrec-main)
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| Challenge: | Existing methods for detecting new intents with labeled data are not cluster-friendly . a robust prototypical attracting learning (RPAL) method is designed to compel instances to gravitate toward their corresponding prototype . |
| Approach: | They propose a robust and adaptive prototypical learning framework for globally distinct decision boundaries for both known and new intent categories. |
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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. |
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. |
Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery (2024.findings-emnlp)
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| Challenge: | Existing methods focus on transferring in-domain (IND) prior knowledge to out-of-domain data through pre-training and clustering. |
| Approach: | They propose a Pseudo-Label enhanced Prototypical Contrastive Learning model for uniformed intent discovery that integrates supervised and pseudo signals from IND and OOD data. |
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Synergizing Large Language Models and Pre-Trained Smaller Models for Conversational Intent Discovery (2024.findings-acl)
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| Challenge: | Current attempts at CID rely on pretrained Small Language Models (SLMs) this lacks the ability to label new intents and is a challenge for small language models. |
| Approach: | They propose to combine Large Language Models (LLMs) with pre-trained SLMs for CID to enhance the semantic comprehension of LLMs. |
| Outcome: | The proposed approach improves the semantic comprehension of LLMs and the operational agility of SLMs by realigning existing descriptors within the SLM’s feature space to correct cluster distortion and promote robust learning of representations. |
Going beyond research datasets: Novel intent discovery in the industry setting (2023.findings-eacl)
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| Challenge: | Novel intent discovery automates grouping of similar messages to identify previously unknown intents. |
| Approach: | They propose to use question-only data to improve the intent discovery pipeline . they propose to utilize conversational structure of real-life datasets for clustering . |
| Outcome: | The proposed method gives 33pp performance boost over state-of-the-art model for question only . it also gives 13pp performance increase over the naive baseline model . |