| Challenge: | Existing methods to classify intents are labor-intensive and time-consuming as intents will be diverse and new intents may be involved. |
| Approach: | They propose a zero-shot intent detection problem which aims to detect emerging user intents where no labeled utterances are currently available. |
| Outcome: | The proposed model can discriminate emerging intents when no labeled utterances are available in training data. |
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Reconstructing Capsule Networks for Zero-shot Intent Classification (D19-1)
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| Challenge: | Existing methods for intent classification are limited due to fast-emerging intents . a recent study shows that existing methods are not effective in recognizing unseen intents. |
| Approach: | They propose to reconstruct capsule networks for zero-shot intent classification by using latent information from labeled utterances. |
| Outcome: | The proposed method outperforms existing methods on two task-oriented dialogue datasets in different languages. |
A Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language Understanding (2021.emnlp-main)
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| Challenge: | Existing systems for speech-based dialogs have found the inadequacy of relying on simple classification techniques to accomplish the automation task. |
| Approach: | They propose a Label-Aware BERT Attention Network (LABAN) for zero-shot multi-intent detection by encoding input utterances with BERT and building a label embedded space by considering embedded semantics in intent labels. |
| Outcome: | The proposed approach can detect many unseen intent labels correctly on a few/zero-shot setting, and achieves state-of-the-art performance on five multi-intent datasets in normal cases. |
Joint Slot Filling and Intent Detection via Capsule Neural Networks (P19-1)
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| Challenge: | Existing models that label slots and detect intent do not preserve hierarchical relationship between words, slots, and intents. |
| Approach: | They propose a capsule-based neural network model which performs slot filling and intent detection via a dynamic routing-by-agreement schema. |
| Outcome: | The proposed model performs better than existing models and existing models on real-world datasets. |
Zero-Shot-BERT-Adapters: a Zero-Shot Pipeline for Unknown Intent Detection (2023.findings-emnlp)
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| Challenge: | Intent discovery remains a crucial task in natural language processing . identifying novel, unseen intents remains one of the biggest challenges in this field . |
| Approach: | They propose a multi-language approach to intent discovery using Adapters and a Transformer architecture. |
| Outcome: | The proposed pipeline outperforms baselines in two zero-shot settings for intent classification and unseen intent discovery. |
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. |
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020.acl-main)
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| Challenge: | Existing methods for unknown intent detection are limited by prior knowledge of class labels. |
| Approach: | They propose to use a Gaussian mixture model to model utterance embeddings with a distribution and inject dynamic class semantic information into Gausssian means. |
| Outcome: | The proposed model performs well on three real task-oriented dialogue datasets in two languages. |
Intent Detection with WikiHow (2020.aacl-main)
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| Challenge: | Existing approaches to intent detection have limited data annotated for new domains or languages. |
| Approach: | They propose to train a set of pretraining intent detection models on wikiHow which can predict a broad range of intended goals from many actions. |
| Outcome: | The proposed models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog 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. |
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection (2020.findings-emnlp)
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| Challenge: | Recent studies show that multi-level matching is difficult due to the scarcity of available annotated utterances. |
| Approach: | They propose a method where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints. |
| Outcome: | The proposed method improves representations of labeled and unlabeled instances while retaining high-level information. |
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. |