Abstract
Relation Extraction (RE) aims to extract semantic relationships in texts from given entity pairs, and has achieved significant improvements. However, in different domains, the RE task can be influenced by various factors. For example, in the financial domain, sentiment can affect RE results, yet this factor has been overlooked by modern RE models. To address this gap, this paper proposes a Sentiment-aware-SDP-Enhanced-Module (SSDP-SEM), a multi-task learning approach for enhancing financial RE. Specifically, SSDP-SEM integrates the RE models with a pluggable auxiliary sentiment perception (ASP) task, enabling the RE models to concurrently navigate their attention weights with the text's sentiment. We first generate detailed sentiment tokens through a sentiment model and insert these tokens into an instance. Then, the ASP task focuses on capturing nuanced sentiment information through predicting the sentiment token positions, combining both sentiment insights and the Shortest Dependency Path (SDP) of syntactic information. Moreover, this work employs a sentiment attention information bottleneck regularization method to regulate the reasoning process. Our experiment integrates this auxiliary task with several prevalent frameworks, and the results demonstrate that most previous models benefit from the auxiliary task, thereby achieving better results. These findings highlight the importance of effectively leveraging sentiment in the financial RE task.
Abstract (translated)
关系抽取（RE）旨在从给定的实体对中提取文本中的语义关系，并已经取得了显著的进步。然而，在不同的领域，如金融领域，RE任务可能受到多种因素的影响，例如情感因素会影响RE结果，但现代RE模型对此却鲜有关注。为了解决这一问题，本文提出了一种情感感知短语依存路径增强模块（SSDP-SEM），这是一种用于增强金融关系抽取的多任务学习方法。具体来说，SSDP-SEM通过插入一个可插拔的情感感知辅助任务，使RE模型能够同时根据文本的情感调整其注意力权重。 首先，我们利用情感模型生成详细的情感标记，并将这些标记插入到实例中。然后，ASP任务专注于捕捉细微的情感信息，通过对情感标记位置的预测来结合情感洞察和短语依存路径（SDP）中的句法信息。此外，这项工作还采用了一种情感注意力瓶颈正则化方法，以规范推理过程。 我们的实验将这种辅助任务与几种流行框架相结合，并发现大多数先前的模型从该辅助任务中受益，从而实现了更好的结果。这些发现强调了在金融关系抽取任务中有效利用情感的重要性。
URL
https://arxiv.org/abs/2506.12452
