ha-lins/MetaLearning4NLP-Papers
A list of recent papers about Meta / few-shot learning methods applied in NLP areas.
This is a curated list of academic papers focusing on advanced techniques in Natural Language Processing (NLP). It collects research on 'meta-learning' and 'few-shot learning' methods that help NLP models perform well even with very little training data. Researchers and practitioners in NLP can use this list to quickly find relevant studies for developing efficient language models.
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Use this if you are an NLP researcher or practitioner looking for academic papers on meta-learning and few-shot learning techniques to improve models for tasks like semantic parsing, dialogue systems, or text classification, especially when working with limited data.
Not ideal if you are looking for ready-to-use software, datasets, or a general introduction to NLP without a specific interest in advanced machine learning methodologies.
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Dec 29, 2020
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