论文标题
自动识别可以用作少量文本分类的标签的单词
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
论文作者
论文摘要
几次射击文本分类的最新方法是将文本输入转换为包含某种形式的任务描述的问题，使用预算的语言模型对其进行处理，并将预测的单词映射到标签。手动定义单词和标签之间的映射需要域专业知识和对语言模型能力的理解。为了减轻此问题，我们设计了一种方法，该方法会自动找到少量培训数据的映射。对于多个任务，我们的方法找到的映射几乎和手工制作的标签到字映射一样。
A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels. Manually defining this mapping between words and labels requires both domain expertise and an understanding of the language model's abilities. To mitigate this issue, we devise an approach that automatically finds such a mapping given small amounts of training data. For a number of tasks, the mapping found by our approach performs almost as well as hand-crafted label-to-word mappings.
