On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning (2022.naacl-main)
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| Challenge: | Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem. |
| Approach: | They propose to modify a few-shot intent detection task to produce a non-trivially strong performance without further domain-specific adaptation. |
| Outcome: | The proposed model improves on the prototypical network variants with task-specific fine-tuning. |
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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
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| Challenge: | Existing paradigms further pre-train language models such as BERT on vast amount of unlabeled corpus, but we find it highly effective and efficient to simply fine-tune BERT with roughly 1,000 labeled utterances from public datasets. |
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| Challenge: | Existing approaches for few-shot text classification rely on exploitation of lexical features and distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. |
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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. |
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