Papers by Haowen Liang
A Closer Look at Few-Shot Out-of-Distribution Intent Detection (2022.coling-1)
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| Challenge: | Existing methods for few-shot out-of-distribution (OOD) intent detection are not adequate . despite its importance, few- shot OOD intent detection is a challenging problem . |
| Approach: | They propose a latent representation generation and self-supervision approach to solve few-shot OOD intent detection problem. |
| Outcome: | The proposed approach is highly effective and could improve state-of-the-art methods for few-shot OOD intent detection. |
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. |
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization (2022.naacl-main)
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| Challenge: | Recent studies show that fine-tuning pre-trained language models with a small set of labeled utterances in a supervised manner is helpful, but it yields an anisotropic feature space, which may suppress the expressive power of the semantic representations. |
| Approach: | They propose to regularize supervised pre-training towards isotropy by contrastive learning and correlation matrix regularizers. |
| Outcome: | The proposed methods improve supervised pre-training by regularizing the feature space towards isotropy. |
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training (2023.findings-acl)
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| Challenge: | Recent progress in intent detection relies on deep models and datasets with well-crafted annotations. |
| Approach: | They propose a continual pre-training approach to train deep learning models . they propose augmentation method and sequential self-distillation to boost performance . |
| Outcome: | The proposed method outperforms methods that employ continual pre-training on labeled datasets on few-shot intent detection tasks. |