Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training (2021.acl-long)
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| Challenge: | Existing methods for out-of-scope intent detection rely on strong assumptions on data distribution and confidence threshold selection. |
| Approach: | They propose a method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training. |
| Outcome: | The proposed method improves on four benchmark dialogue datasets and improves over state-of-the-art methods. |
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Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification (2024.lrec-main)
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| Challenge: | Current methods for intent classification often rely on assumptions about data distributions and outliers are unpredictable . |
| Approach: | They propose a dual encoder for threshold-based re-classification that generates user utterance embeddings and incorporates out-of-scope phrases from open-domain datasets. |
| Outcome: | The proposed framework outperforms benchmarks on the CLINC-150, Stackoverflow, and Banking77 datasets and achieves an increase of up to 13% and 5% in F1 score for known and unknown intents. |
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. |
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction (D19-1)
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Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, Jason Mars
| Challenge: | Task-oriented dialog systems need to know when a query falls outside their range of supported intents. |
| Approach: | They propose a dataset that includes queries that are out-of-scope and 150 intent classes over 10 domains. |
| Outcome: | The proposed dataset includes queries that are out-of-scope, i.e., queries that do not fall into any of the system’s supported intents. |
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification (2024.lrec-main)
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| Challenge: | Existing studies have not studied the performance of intent classifiers against hard-negative out-of-scope utterances. |
| Approach: | They propose to generate hard-negative OOS data using ChatGPT and evaluate them against three benchmark intent classifiers. |
| Outcome: | The proposed method improves classifiers' robustness against hard-negative out-of-scope utterances and general OOS data. |
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning (2021.acl-short)
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| Challenge: | Existing methods of OOD detection only focus on whether a sample is correctly classified . lack of real OOD examples leads to poor prior knowledge about these unknown intents . |
| Approach: | They propose a supervised contrastive learning objective to minimize intra-class variance . they employ an adversarial augmentation mechanism to obtain pseudo diverse views . |
| Outcome: | The proposed method minimizes intra-class variance by pulling together in-domain intents belonging to the same class and maximizes inter-class variation by pushing apart samples from different classes. |
Intent Detection in the Age of LLMs (2024.emnlp-industry)
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| Challenge: | Traditional approaches to intent detection struggle with out-of-scope (OOS) detection. |
| Approach: | They propose to use adaptive in-context learning and chain-of-thought prompting to detect intent in SOTA LLMs. |
| Outcome: | The proposed system achieves 2% of native accuracy with 50% less latency. |
KNN-Contrastive Learning for Out-of-Domain Intent Classification (2022.acl-long)
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| Challenge: | Existing methods for OOD intent classification are limited to regions with compact or simply-connected features, which assumes no OOD intentions reside. |
| Approach: | They propose a method that uses k-nearest neighbors to learn discriminative semantic features that are more conducive to OOD detection. |
| Outcome: | The proposed method improves OOD detection performance while requiring no restrictions on feature distribution. |
Navigating the Unknown: Intent Classification and Out-of-Distribution Detection Using Large Language Models (2025.findings-emnlp)
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| Challenge: | Out-of-Distribution (OOD) detection requires great generalization capability . |
| Approach: | They propose a method that is cost-efficient, high-performing, highly robust and versatile enough to be used with smaller LLMs without sacrificing performance. |
| Outcome: | The proposed method is cost-efficient, high-performing, robust, and versatile enough to be used with smaller LLMs without sacrificing performance. |
Towards Open Environment Intent Prediction (2023.findings-acl)
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| Challenge: | Out-of-Domain (OOD) Intent Classification and New Intent Discovering are two tasks in the Task-Oriented Dialogue System. |
| Approach: | They propose a task paradigm to extend Out-of-Domain (OOD) Intent Classification and New Intent Discovering tasks in the Task-Oriented Dialogue System. |
| Outcome: | The proposed scheme improves on existing OOD intent classification and discovery datasets. |
From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (2024.findings-acl)
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| Challenge: | Existing studies fine-tune discriminative models on specific defined intent classes, preventing them from being directly adopted to new intent domains. |
| Approach: | They propose to use a pre-trained generative intent model to detect new intents from different domains with no parameter updates. |
| Outcome: | The proposed model outperforms baselines that need further fine-tuning or domain-specific samples. |