通过规则生成与数据合成迭代优化文本分类效果
ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification
- 用语法n-gram归纳规则，结合自举法不断优化数据与规则
- 在少样本和多语言场景下均提升模型性能，部分超越对比学习
- 适合需要少标注数据的文本分类任务，尤其对低资源语言有效
我们提出ARISE框架，通过迭代式规则归纳与合成数据生成，提升文本分类性能。该框架结合合成数据生成与自动规则归纳，利用自举法持续过滤无效规则与数据。规则基于语法n-gram的归纳性泛化，提供互补的监督信号。仅使用规则即在上下文学习（ICL）和微调（FT）设置中带来性能提升；仅使用增强数据也优于复杂方法如对比学习。我们在涵盖三种全样本、八种少样本和七种多语言变体设置的多个数据集上进行广泛实验，结果表明生成的规则与数据在多种领域和语言下均有效提升模型表现。
原文摘要 · Abstract (English)
We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction, via bootstrapping, to iteratively filter the generated rules and data. We induce rules via inductive generalisation of syntactic n-grams, enabling us to capture a complementary source of supervision. These rules alone lead to performance gains in both, in-context learning (ICL) and fine-tuning (FT) settings. Similarly, use of augmented data from ARISE alone improves the performance for a model, outperforming configurations that rely on complex methods like contrastive learning. Further, our extensive experiments on various datasets covering three full-shot, eight few-shot and seven multilingual variant settings demonstrate that the rules and data we generate lead to performance improvements across these diverse domains and languages.
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