# Diverse Few-Shot Text Classification with Multiple Metrics

arXiv: https://arxiv.org/abs/1805.07513 · HTML: https://mlsanity.com/paper/1805.07513/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2018.

## Built on by (most cited first, 30 of 61)

- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Meta-learning via Language Model In-context Tuning](https://mlsanity.com/paper/2110.07814/) (2021)
- [Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2108.13161/) (2021)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [FLEX: Unifying Evaluation for Few-Shot NLP](https://mlsanity.com/paper/2107.07170/) (2021)
- [Multi-Source Domain Adaptation with Mixture of Experts](https://mlsanity.com/paper/1809.02256/) (2018)
- [Discrete and Soft Prompting for Multilingual Models](https://mlsanity.com/paper/2109.03630/) (2021)
- [Few-shot Intent Classification and Slot Filling with Retrieved Examples](https://mlsanity.com/paper/2104.05763/) (2021)
- [Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks](https://mlsanity.com/paper/2309.13256/) (2023)
- [Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System](https://mlsanity.com/paper/2104.11882/) (2021)
- [Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes](https://mlsanity.com/paper/2104.08481/) (2021)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Revisiting Self-Training for Few-Shot Learning of Language Model](https://mlsanity.com/paper/2110.01256/) (2021)
- [Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning](https://mlsanity.com/paper/2203.09137/) (2022)
- [Label Semantics for Few Shot Named Entity Recognition](https://mlsanity.com/paper/2203.08985/) (2022)
- [Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization](https://mlsanity.com/paper/2205.07208/) (2022)
- [Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind](https://mlsanity.com/paper/2211.04684/) (2022)
- [Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification](https://mlsanity.com/paper/2107.12262/) (2021)
- [ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification](https://mlsanity.com/paper/2305.09269/) (2023)
- [CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models](https://mlsanity.com/paper/2302.04030/) (2023)
- [Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings](https://mlsanity.com/paper/2410.13153/) (2024)
- [Semantic-aligned Fusion Transformer for One-shot Object Detection](https://mlsanity.com/paper/2203.09093/) (2022)
- [Cluster & Tune: Boost Cold Start Performance in Text Classification](https://mlsanity.com/paper/2203.10581/) (2022)
- [Few-Shot Learning on Graphs](https://mlsanity.com/paper/2203.09308/) (2022)
- [Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2303.02513/) (2023)
- [A Comprehensive Review of Few-shot Action Recognition](https://mlsanity.com/paper/2407.14744/) (2024)
- [Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP](https://mlsanity.com/paper/2111.01322/) (2021)
- [An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2110.13691/) (2021)
- [ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification](https://mlsanity.com/paper/2101.11753/) (2021)

## Abstract (arXiv)

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.