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Compared to many\nexisting works that apply either metric-based or optimization-based\nmeta-learning to image domain with low inter-task variance, we consider a more\nrealistic setting, where tasks are diverse. However, it imposes tremendous\ndifficulties to existing state-of-the-art metric-based algorithms since a\nsingle metric is insufficient to capture complex task variations in natural\nlanguage domain. To alleviate the problem, we propose an adaptive metric\nlearning approach that automatically determines the best weighted combination\nfrom a set of metrics obtained from meta-training tasks for a newly seen\nfew-shot task. Extensive quantitative evaluations on real-world sentiment\nanalysis and dialog intent classification datasets demonstrate that the\nproposed method performs favorably against state-of-the-art few shot learning\nalgorithms in terms of predictive accuracy. We make our code and data available\nfor further study.","url_abs":"http://arxiv.org/abs/1805.07513v1","url_pdf":"http://arxiv.org/pdf/1805.07513v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"diverse-few-shot-text-classification-with","repo_url":"https://github.com/Gorov/DiverseFewShot_Amazon","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"diverse-few-shot-text-classification-with","repo_url":"https://github.com/zhongyuchen/few-shot-text-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"few-shot-text-classification","task_name":"Few-Shot Text Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"intent-classification","task_name":"Intent Classification"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"intent-classification-1","task_name":"intent-classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.07513","atlas_url":"https://app.syntology.ai/?focus=1805.07513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07513"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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