Exploring Zero and Few-shot Techniques for Intent Classification (2023.acl-industry)
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| Challenge: | Intent classification is the primary natural language understanding task for a virtual agent or a chatbot. |
| Approach: | They propose four different approaches to zero-shot intent classification with low-resource constraints . they use domain adaptation, data augmentation, and parametric fine-tuning to achieve this . |
| Outcome: | The proposed approaches perform well in low-resource settings for zero/few-shot intent classification . the proposed methods remove or substantially reduce the work to provide intent-utterances . |
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| Challenge: | Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. |
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| Challenge: | Recent studies have focused on the problem of generalizing from a few examples per category. |
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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. |
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| Challenge: | a tutorial aims to introduce NLP researchers to the latest techniques for learning from little-to-no data . aims at bringing interested researchers up to speed about the latest and ongoing techniques . |
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| Challenge: | Large pre-trained language models (LMs) have a surprising ability to perform zero-shot learning. |
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