# A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction

arXiv: https://arxiv.org/abs/2506.12452 · HTML: https://mlsanity.com/paper/2506.12452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024)
- [TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task](https://mlsanity.com/paper/2004.14855/) (2020)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [Attention Guided Graph Convolutional Networks for Relation Extraction](https://mlsanity.com/paper/1906.07510/) (2019)
- [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021)
- [Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path](https://mlsanity.com/paper/1508.03720/) (2015)
- [A Causal View of Entity Bias in (Large) Language Models](https://mlsanity.com/paper/2305.14695/) (2023)
- [SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News](https://mlsanity.com/paper/2305.12257/) (2023)
- [REFinD: Relation Extraction Financial Dataset](https://mlsanity.com/paper/2305.18322/) (2023)
- [KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents](https://mlsanity.com/paper/2210.09163/) (2022)
- [On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction](https://mlsanity.com/paper/2402.18061/) (2024)
- [iMETRE: Incorporating Markers of Entity Types for Relation Extraction](https://mlsanity.com/paper/2307.00132/) (2023)
- [Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction](https://mlsanity.com/paper/2308.04534/) (2023)
- [Nearest Neighbor Search over Vectorized Lexico-Syntactic Patterns for Relation Extraction from Financial Documents](https://mlsanity.com/paper/2310.17714/) (2023)
- [Multi-Task Learning for Features Extraction in Financial Annual Reports](https://mlsanity.com/paper/2404.05281/) (2024)
- [Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech](https://mlsanity.com/paper/2405.06665/) (2024)

## Abstract (arXiv)

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.