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. |
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New Intent Discovery with Pre-training and Contrastive Learning (2022.acl-long)
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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. |
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 . |
| Outcome: | The proposed approach outperforms baselines on three intent-related tasks on multiple datasets. |
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. |
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection (2022.findings-naacl)
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| Challenge: | Existing methods to identify all possible user intents at design time are expensive and require storage of past data. |
| Approach: | They propose to continually train an intent detector on new intents while maintaining performance on prior intents. |
| Outcome: | The proposed method outperforms exemplar replay-based approaches on lifelong intent detection tasks and achieves state-of-the-art on four public datasets. |
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 . |
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. |
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. |
| Outcome: | The proposed method has been proven effective in two different settings of discovering new intents. |
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)
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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
| Challenge: | Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models. |
| Approach: | They propose a few-shot intent detection schema using contrastive pre-training and fine-tuning. |
| Outcome: | The proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings. |
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. |
Adversarial Self-Supervised Learning for Out-of-Domain Detection (2021.naacl-main)
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| Challenge: | Existing methods for detecting out-of-domain (OOD) intents are unsupervised and require extensive labeled data. |
| Approach: | They propose a self-supervised contrastive learning framework to model discriminative semantic features from unlabeled data. |
| Outcome: | The proposed framework outperforms baseline methods on two public benchmark datasets with a statistically significant margin. |