## 4 Methodology
### Multi-Task Learning  
Beyond the primary task of nowcasting interest rate direction, central bank communication texts often contain subtle signals about future policy leanings, commonly interpreted by the market as \"hawkish\" (inclined towards tightening monetary policy, e.g., raising rates) or \"dovish\" (inclined towards easing, e.g., cutting rates or maintaining low rates). This policy \"tone\" or sentiment is crucial for understanding the deeper meaning of the text and predicting future rate movements.  
To enable the model to capture and utilize these latent signals more effectively, we introduce an auxiliary task:hawkish/dovish classification of policy statement or speech paragraphs. We leverage the \"Trillion Dollar Words\" dataset, which offers paragraph-levelhawkish/dovish annotations for FOMC texts. We employ this binary classification task as an auxiliary task, training it jointly with the main three-class interest rate prediction task.  
In the multi-task learning framework, the primary interest rate prediction task and the auxiliary hawkish/dovish classification task share most of the underlying Hyena feature extractor network structure \(G_{f}\) (including the parts fine-tuned via Delta-LoRA). At a higher level of \(G_{f}\) (e.g., the output of the second-to-last Hyena block), the network structure branches into two independent task-specific heads: 1. Nowcasting head (\(G_{y}\)): typically one or more fully connected layers followed by a Softmax layer, outputting probabilities for the three classes (up/down/stable). Its loss function is \(\mathcal{L}_{\text{now}}\) (e.g., weighted cross-entropy loss). 2. Hawkish/dovish head (\(G_{hawk}\)): similarly, one or more fully connected layers followed by a Sigmoid or Softmax layer (binary classification here), outputting hawkish/dovish probabilities. Its loss function is \(\mathcal{L}_{\text{hawk}}\) (e.g., binary cross-entropy loss).  
The model's total loss function \(\mathcal{L}_{\text{total}}\) is a weighted sum of the main task loss, the auxiliary task loss, and the aforementioned domain adversarial loss:  
\[\mathcal{L}_{\text{total}}=\mathcal{L}_{\text{now}}+\lambda_{\text{hawk}} \mathcal{L}_{\text{hawk}}+\lambda_{\text{dann}}\mathcal{L}_{\text{dam}} \tag{10}\]  
where \(\lambda_{\text{hawk}}\) and \(\lambda_{\text{dann}}\) are hyperparameters balancing the importance of different tasks. Based on our experiments and framework setup, we set \(\lambda_{\text{hawk}}=0.3\). The weight \(\lambda_{\text{dann}}\) employs a linear warm-up strategy, gradually increasing from 0 to 1 during the initial phase of training, then remaining constant. This warm-up strategy helps stabilize feature learning early in training and prevents the domain adversarial component from prematurely dominating the gradients.  
By jointly training these related tasks, we expect to achieve implicit regularization, as the auxiliary task compels the shared feature extractor \(G_{f}\) to learn more general and robust representations capable of reflecting policy tones alongside predicting the final rate decision. Learning to distinguish hawkish/dovish tendencies helps the model to gain a deeper understanding of the text's semantic and sentiment nuances, potentially improving interest rate prediction accuracy indirectly. Our preliminary validation indicated that adding the hawkish/dovish auxiliary task yielded an approximate 0.6 pp improvement in AUC on the validation set. Furthermore, the trained auxiliary task head itself can provide valuable quantitative metrics on policy statement sentiment. In conclusion, the multi-task learning framework enriches the model's learning objectives by introducing relevant auxiliary signals, contributing to improved performance on the core interest rate prediction task and enhancing the model's overall generalization ability.