# Diverse Few-Shot Text Classification with Multiple Metrics Source: [Diverse Few-Shot Text Classification with Multiple Metrics]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:02:11.964Z Authors: Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou Published: 2018-05-19T04:45:04.000Z ## Abstract We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more realistic setting, where tasks are diverse. However, it imposes tremendous difficulties to existing state-of-the-art metric-based algorithms since a single metric is insufficient to capture complex task variations in natural language domain. To alleviate the problem, we propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. Extensive quantitative evaluations on real-world sentiment analysis and dialog intent classification datasets demonstrate that the proposed method performs favorably against state-of-the-art few shot learning algorithms in terms of predictive accuracy. We make our code and data available for further study. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Few-shot Sentiment Classification on Amazon Review Sentiment Classification (test)](): Few-shot Sentiment Classification; Amazon Review Sentiment Classification (ARSC) (test); Mean Accuracy: 83.12. ## Original sources and implementation [Original paper]() [PDF]()