使用蕴含式建模解决文本分类中的概念偏移问题
Tackling Concept Shift in Text Classification using Entailment-style Modeling
AI总结：
针对文本分类中类别定义随时间变化的概念偏移问题，提出将普通分类重构为蕴含式建模的方法，在少样本场景下实现显著F1提升，且部署后可节省75%标注成本。
摘要：
在自然语言处理（NLP）领域，预训练语言模型（PLMs）已在文本分类（TC）问题中取得了巨大成功。在许多现实世界的文本分类任务中，待学习的类别定义并非一成不变，而是会随时间发生变化，这种现象被称为概念偏移（Concept Shift）。\n大多数应对概念偏移的技术都依赖于用新标注的数据重新训练旧分类器。然而，要针对新概念微调大型深度学习模型，所需的训练数据量极大，相关的标注成本可能高得令人望而却步，且十分耗时。\n在本研究中，我们提出了一种重构方案：将普通分类任务转化为蕴含式问题，这种方式仅需少得多的数据即可重新训练文本分类器，使其适应新概念。我们在真实数据集和合成数据集上都验证了所提方法的有效性：在少样本设置下，两类数据集的F1值绝对提升分别可达7%和40%。此外，在部署后，我们的方案总体上还帮助节省了75%的标注成本。
英文摘要：
Pre-trained language models (PLMs) have seen tremendous success in text classification (TC) problems in the context of Natural Language Processing (NLP). In many real-world text classification tasks, the class definitions being learned do not remain constant but rather change with time - this is known as Concept Shift. Most techniques for handling concept shift rely on retraining the old classifiers with the newly labelled data. However, given the amount of training data required to fine-tune large DL models for the new concepts, the associated labelling costs can be prohibitively expensive and time consuming. In this work, we propose a reformulation, converting vanilla classification into an entailment-style problem that requires significantly less data to re-train the text classifier to adapt to new concepts. We demonstrate the effectiveness of our proposed method on both real world & synthetic datasets achieving absolute F1 gains upto 7% and 40% respectively in few-shot settings. Further, upon deployment, our solution also helped save 75% of labeling costs overall.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
