## 7 Conclusions  
In conclusion, this research contributes on three main fronts: methodological innovation, dataset construction, and empirical performance validation. We proposed and validated a novel integrated deep learning framework combining Hyena Hierarchy for long sequence modeling, Delta-LoRA for parameter-efficient fine-tuning, domain adaptation techniques, and multi-task learning. This framework systematically addresses the core challenges encountered in analyzing central bank communication texts, namely ultra-long sequence processing, efficient model adaptation, and cross-domain generalization. Concurrently, we constructed and annotated a large-scale, cross-institutional dataset covering decades of official communications from the U.S. Federal Open Market Committee and the European Central Bank.  
The empirical results clearly demonstrate that our proposed complete model achieves state-of-the-art performance in the interest rate nowcasting task compared to strong baseline models, showing significant improvements in key metrics such as accuracy, macro F1 score, and AUC. Furthermore, it drastically reduces the inference time required for processing long texts. The combination of the Hyena architecture and the Delta-LoRA fine-tuning technique, in particular, showcases its superiority and significant potential for tackling tasks involving the analysis of lengthy, information-dense texts characteristic of the financial and macroeconomic policy domains.  
We believe this work opens new possibilities for leveraging advanced natural language processing techniques to gain deeper insights from central bank communications, aiding economic forecasting and policy analysis. Future research should continue exploring more powerful model architectures, richer multimodal information fusion, and broader cross-lingual and cross-institutional applications. We also call for enhanced collaboration between academia and industry to jointly build and maintain more comprehensive, standardized, and open benchmark resources for central bank communication text analysis, fostering continued progress and knowledge sharing in this interdisciplinary field.  
Conceptualization, T.S., S.Y. and R.Z.; methodology, T.S. and S.Y.; software, T.S. and S.Y.; validation, T.S., S.Y. and R.Z.; formal analysis, T.S. and S.Y.; investigation, T.S. and S.Y.; resources, R.Z.; data curation, T.S. and S.Y.; writing--original draft preparation, T.S.; writing--review and editing, T.S., S.Y. and R.Z.; visualization, T.S. and S.Y.; supervision, R.Z.; project administration, R.Z.; funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used in this study are available from the corresponding author upon reasonable request. Raw data sources are publicly available as cited in the manuscript.  
The authors would like to thank the anonymous reviewers for their helpful and constructive comments which greatly contributed to the improvement of the final version of the paper.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.