UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining

Jinan Zou, Hai Cao, Yanxi Liu, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi
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Abstract

Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media. This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022. There are 2 sub-tasks in ERAI. Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification. Sub-task 2 is an unsupervised learning task ranking the opinions’ maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values. The proposed approaches achieve competitive accuracy of 54.02% on ML Accuracy and 51.72% on MPP Accuracy for pairwise tasks, also 12.35% and -9.39% regression unsupervised ranking task for MPP and ML.
FinNLP-2022 ERAI任务中的UOA:利用类标签描述进行财务意见挖掘
评估业余投资者的基本原理(ERAI)是一项从社交媒体中挖掘专家观点的任务。本文总结了我们对ERAI共享任务的解决方案,该任务与EMNLP 2022的FinNLP研讨会位于同一地点。在ERAI中有2个子任务。子任务1是配对比较任务,我们提出了一个基于bert的预训练模型,在公共空间中投射意见对进行分类。子任务2是对意见的最大潜在利润(MPP)和最大潜在损失(ML)进行排序的无监督学习任务,其中我们的模型利用回归方法和多层感知器对MPP和ML值进行排序。所提出的方法在成对任务的ML准确度和MPP准确度上的竞争准确率分别为54.02%和51.72%,在MPP和ML的回归无监督排序任务上的竞争准确率分别为12.35%和-9.39%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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