Search Results for author: Haowen Liang

Found 4 papers, 2 papers with code

A Closer Look at Few-Shot Out-of-Distribution Intent Detection

1 code implementation COLING 2022 Li-Ming Zhan, Haowen Liang, Lu Fan, Xiao-Ming Wu, Albert Y.S. Lam

Comprehensive experiments on three real-world intent detection benchmark datasets demonstrate the high effectiveness of our proposed approach and its great potential in improving state-of-the-art methods for few-shot OOD intent detection.

Intent Detection Task-Oriented Dialogue Systems

Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training

no code implementations8 Jun 2023 Haode Zhang, Haowen Liang, LiMing Zhan, Xiao-Ming Wu, Albert Y. S. Lam

We consider the task of few-shot intent detection, which involves training a deep learning model to classify utterances based on their underlying intents using only a small amount of labeled data.

intent-classification Intent Classification +2

Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training

no code implementations ACL 2021 Li-Ming Zhan, Haowen Liang, Bo Liu, Lu Fan, Xiao-Ming Wu, Albert Y. S. Lam

Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection.

Intent Detection Outlier Detection +1

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