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. |
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| Challenge: | Existing methods for out-of-scope intent detection rely on strong assumptions on data distribution and confidence threshold selection. |
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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 . |
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