论文标题
自适应及时基于学习的少量情感分析
Adaptive Prompt Learning-based Few-Shot Sentiment Analysis
论文作者
论文摘要
在自然语言处理领域，通过使用大型标记的数据集，通过深度学习的情感分析具有出色的表现。同时，在许多情感分析中，标记的数据不足，并且获得这些数据是耗时且费力的。提示学习促进通过提示来重新重新完成下游任务来解决数据缺陷。这样，适当的提示对于模型的性能非常重要。本文提出了使用SEQ2SEQ意见结构的自适应提示（AP）构建策略，以获取输入序列的语义信息。然后动态构建自适应提示，不仅可以提高提示的质量，而且可以通过预训练的提示有效地推广到其他字段，该提示是由现有公共标记的数据构建的。几clue数据集的实验结果表明，所提出的方法AP可以有效地构建适当的适应性提示，而不管手工制作的及时质量和表现优于最先进的基线。
In the field of natural language processing, sentiment analysis via deep learning has a excellent performance by using large labeled datasets. Meanwhile, labeled data are insufficient in many sentiment analysis, and obtaining these data is time-consuming and laborious. Prompt learning devotes to resolving the data deficiency by reformulating downstream tasks with the help of prompt. In this way, the appropriate prompt is very important for the performance of the model. This paper proposes an adaptive prompting(AP) construction strategy using seq2seq-attention structure to acquire the semantic information of the input sequence. Then dynamically construct adaptive prompt which can not only improve the quality of the prompt, but also can effectively generalize to other fields by pre-trained prompt which is constructed by existing public labeled data. The experimental results on FewCLUE datasets demonstrate that the proposed method AP can effectively construct appropriate adaptive prompt regardless of the quality of hand-crafted prompt and outperform the state-of-the-art baselines.
