Research On Intelligent Dialogue System For Financial Complianc

The emergence of artificial intelligence technology has led to the adoption of intelligent dialogue systems in many industries.In the field of financial compliance management,as regulatory policies become increasingly stringent and financial businesses become more complex,the workload of daily compliance management has become overwhelming.Therefore,applying intelligent dialogue systems to the field of financial compliance management can effectively enhance the work efficiency of compliance management personnel,alleviate their workload,and promote the development of the entire industry.Currently,there are two main types of intelligent dialogue systems in the field of financial compliance management: rule-based dialogue systems and deep learning-based dialogue systems.Rule-based dialogue systems require significant upfront investment in business rules and system construction,but once constructed,they can only be used in fixed business scenarios,with limited adaptability to new situations.Conversely,deep learning-based dialogue systems demand a large amount of training data and often have poor interpretability,which hampers user adoption and analysis by business personnel.This paper aims to address the challenge of limited training data and the need for strong interpretability in financial compliance by proposing a deep learning-based dialogue system specifically tailored for the financial compliance domain.The proposed system’s key contributions are twofold:(1)In the context of insufficient annotated data in financial compliance,we propose a method based on dynamic routing induction networks to build an intention classification module.Dynamic routing induction networks reconstruct hierarchical semantic representations of various samples,dynamically inducing category features from sample information.This approach effectively resolves the issue of limited annotated data.The dynamic routing induction network has the ability to induce category features of samples,ignoring irrelevant details and noise in training samples that are unrelated to professional knowledge,and abstracting semantic representations of categories from multiple expressions of training samples,thereby possessing small sample learning capability.Experiments on authentic financial compliance intent recognition datasets reveal that our method of constructing an intention classification model using dynamic routing induction networks improves accuracy by4%-5% compared to classical small-sample intention classification models like Matching Networks.(2)In the context of slot filling,which requires both entity extraction and relationship extraction,we propose a prompt learning-based method to construct a unified model capable of completing both tasks simultaneously.First,a structured extraction language is proposed to unify the output format of entity and relationship extractions,laying the foundation for unified modeling.Second,prompt learning templates are designed to use user query inputs and prompt learning templates jointly as model inputs,enabling the model to adaptively control the task to be executed and output corresponding structured content for the task.Third,a powerful encoder-decoder model with strong representation and generation capabilities is designed to extract slot information from user query inputs.Experiments on real financial compliance slot extraction datasets show that our prompt learning-based method of building a slot filling unified model reduces modeling costs and improves accuracy by approximately 1% compared to separate modeling of entity extraction and relationship extraction tasks.(3)In the construction of compliance knowledge bases,the multi-label mapping module typically requires high accuracy and strong interpretability.To this end,we propose a method of constructing a compliance knowledge base module based on regular expression neural networks.By transforming regular expressions into finite state automata that express equivalent semantics and then modeling them as recurrent neural networks,we obtain equivalent recurrent neural networks that have the advantages of being trainable to achieve higher accuracy while also being transformed into equivalent regular expressions to obtain interpretability.The regular expression neural network constructed in this manner achieves high accuracy and strong interpretability,satisfying the requirements of building a compliance knowledge base.Experiments on authentic financial compliance knowledge base datasets show that our method of constructing a compliance knowledge base module based on regular expression neural networks has strong interpretability and comparable accuracy compared to classic neural network models.