## 1 Introduction  
In today's increasingly interconnected and complex global economy, the communication strategies of central banks have become central tools for executing monetary policy and managing market expectations [1, 2]. Whether it is the policy statements and meeting minutes released by the U.S. Federal Open Market Committee (FOMC) or the speeches and press conference transcripts from the European Central Bank (ECB) President, these English-language texts convey critical information about future economic trends, inflation prospects, and interest rate paths [3]. Financial market participants, from large institutional investors to the general public, heavily rely on accurate interpretations of these communications to make investment decisions and assess risks. Therefore, conducting timely and in-depth quantitative analysis of central bank communication texts, particularly for nowcasting the short-term direction of interest rate changes following their release, holds significant theoretical importance and immense practical value. This can provide forward-looking insights before the release of official economic data, which is crucial for navigating rapidly changing market dynamics. However, leveraging natural language processing (NLP) techniques to extract reliable predictive signals from central bank communication texts presents a series of severe and unique challenges. While traditional econometric models and classical machine learning approaches have been extensively applied to financial forecasting, they face fundamental limitations when confronted with the complexity of contemporary central bank communications. Conventional econometric models, such as Vector Autoregression (VAR) and linear regression frameworks, rely primarily on structured numerical data and predefined variables, making them inadequate for processing the rich, unstructured textual information embedded in policy statements and speeches. Similarly, classical machine learning techniques like Support Vector Machines or logistic regression with TF-IDF features can only capture shallow linguistic patterns, and fail to understand the nuanced, context-dependent meanings inherent in central bank language. These approaches are particularly challenged by the lengthy nature of policy documents, which often exceed 10,000 tokens and contain complex inter-sentence dependencies that require sophisticated sequential modeling capabilities that are beyond the scope of traditional methods. First, the nature of the data itself poses a substantial obstacle. Official central bank documents, especially in-depth reports like FOMC meeting minutes, are exceptionally lengthy, often exceeding ten thousand tokens. This far surpasses the context length that current mainstream NLP models, particularly those based on the standard Transformer architecture [4], can effectively handle. The self-attention mechanism widely used in these models has computational and memory complexity quadratic in sequence length (\(O(n^{2})\)) [5], making direct processing of such long, complete documents computationally prohibitive or infeasible. The existing work often resorts to compromise strategies like truncating input, analyzing only summaries, or performing sentence-level processing [6; 7], but this inevitably sacrifices document-level global context, long-range argumentative logic, and subtle tones that might permeate the entire text, thereby compromising information integrity and analytical depth. Simultaneously, the language used by central banks is often highly specialized, cautiously worded, and filled with nuances [8]. Policymakers meticulously weigh every word, using precise economic terminology, complex conditional clauses, and subtle tonal variations to convey intricate policy intentions and risk assessments.