BvSP：面向少样本方面情感四元组预测的宽视角软提示方法
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction
AI总结：
本文针对少样本方面情感四元组预测问题，提出宽视角软提示方法BvSP，通过多模板相关性选择、软提示引导和投票聚合，并构建FSQP数据集，在多个少样本设置下显著超越现有方法。
摘要：
方面情感四元组预测（ASQP）旨在预测四个基于方面的元素，包括方面词、观点词、方面类别和情感极性。在实际应用中，由于数据分布不同，未见的方面会给训练好的神经模型带来许多挑战。受此启发，本文将ASQP形式化为少样本场景，旨在实现实际应用中的快速适应。因此，我们首先构建了一个少样本ASQP数据集（FSQP），该数据集包含更丰富的类别，并且在少样本研究中更加均衡。此外，最近的方法通过生成范式提取四元组，该范式涉及将输入句子转换为模板化的目标序列。然而，它们主要关注单一模板的利用或不同模板顺序的考虑，从而忽视了各种模板之间的相关性。为了解决这个问题，我们进一步提出了一种宽视角软提示（BvSP）方法，该方法通过考虑不同模板之间的相关性，以更宽的视角聚合多个模板。具体来说，BvSP使用预训练语言模型通过Jensen-Shannon散度选择最相关的k个模板。BvSP进一步引入软提示，利用所选模板来引导预训练语言模型。然后，我们通过投票机制聚合多模板的结果。实验结果表明，BvSP在四种少样本设置和其他公开数据集上显著优于最先进的方法。我们的代码和数据集可在https://github.com/byinhao/BvSP获取。
英文摘要：
Aspect sentiment quad prediction (ASQP) aims to predict four aspect-based elements, including aspect term, opinion term, aspect category, and sentiment polarity. In practice, unseen aspects, due to distinct data distribution, impose many challenges for a trained neural model. Motivated by this, this work formulates ASQP into the few-shot scenario, which aims for fast adaptation in real applications. Therefore, we first construct a few-shot ASQP dataset (FSQP) that contains richer categories and is more balanced for the few-shot study. Moreover, recent methods extract quads through a generation paradigm, which involves converting the input sentence into a templated target sequence. However, they primarily focus on the utilization of a single template or the consideration of different template orders, thereby overlooking the correlations among various templates. To tackle this issue, we further propose a Broadview Soft Prompting (BvSP) method that aggregates multiple templates with a broader view by taking into account the correlation between the different templates. Specifically, BvSP uses the pre-trained language model to select the most relevant k templates with Jensen-Shannon divergence. BvSP further introduces soft prompts to guide the pre-trained language model using the selected templates. Then, we aggregate the results of multi-templates by voting mechanism. Empirical results demonstrate that BvSP significantly outperforms the stateof-the-art methods under four few-shot settings and other public datasets. Our code and dataset are available at https://github.com/byinhao/BvSP.
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。
