论文标题
逆更好！快速准确提示几个杆插槽标记
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging
论文作者
论文摘要
促使方法最近在几次学习中取得了令人印象深刻的成功。这些方法用及时的句子修改输入样本，并将标签令牌解码以将样本映射到相应的标签。但是，对于插槽标记的任务，这种范式非常低效。由于插槽标记样本是句子中多个连续的单词，因此提示方法必须枚举所有n-grams令牌跨度以找到所有可能的插槽，这会大大减慢预测。为了解决这个问题，我们引入了一个逆范式来提示。不同于经典提示将令牌映射到标签，我们反向预测给定插槽类型的插槽值。这种反向提示仅需要对每种插槽类型的一转预测，并且可以极大地提高预测。此外，我们提出了一种新颖的迭代预测策略，该模型从中学会通过考虑不同的插槽类型之间的关系来完善预测。我们发现，令人惊讶的是，提出的方法不仅可以更快地预测速度，而且可以显着改善效果（在10次设置上提高了6.1个F1分数），并实现了新的最先进的性能。
Prompting methods recently achieve impressive success in few-shot learning. These methods modify input samples with prompt sentence pieces, and decode label tokens to map samples to corresponding labels. However, such a paradigm is very inefficient for the task of slot tagging. Since slot tagging samples are multiple consecutive words in a sentence, the prompting methods have to enumerate all n-grams token spans to find all the possible slots, which greatly slows down the prediction. To tackle this, we introduce an inverse paradigm for prompting. Different from the classic prompts mapping tokens to labels, we reversely predict slot values given slot types. Such inverse prompting only requires a one-turn prediction for each slot type and greatly speeds up the prediction. Besides, we propose a novel Iterative Prediction Strategy, from which the model learns to refine predictions by considering the relations between different slot types. We find, somewhat surprisingly, the proposed method not only predicts faster but also significantly improves the effect (improve over 6.1 F1-scores on 10-shot setting) and achieves new state-of-the-art performance.
