## 3 Results and Discussion
### Discussion  
The experimental results indicate that all three sequential heads achieved competitive performance within the Siamese framework for job-candidate matching. However, several notable observations can be drawn from the quantitative and qualitative analyses performed.  
Interpretation of Results.  
Across multiple evaluation metrics, including Top-K accuracy, Mean Reciprocal Rank (MRR), and embedding space separability, the Transformer-based head consistently demonstrated the strongest performance. In particular, it achieved the highest Top-1 accuracies and placed correct candidates near the top of the ranking more reliably (higher MRR) compared to GRU and LSTM. This finding suggests that the self-attention mechanism in Transformers captures long-range semantic relationships between job descriptions and candidate resumes more effectively. Both GRU and LSTM showed fast initial convergence and performed well on smaller \(K\) values (e.g., Top-1 and Top-3), highlighting that recurrent gating mechanisms can quickly learn relevant features in textual data. Still, their overall final performance lagged slightly behind the Transformer head when considering broader ranking scenarios (e.g., Top-20 and Top-50).  
Comparison to Related Work.  
Our results align with the existing literature, where Transformers often outperform recurrent models on various NLP tasks due to their superior capacity for learning global dependencies. Many prior studies on text similarity, information retrieval, and recommendation have reported similar advantages of attention-based architectures, especially in scenarios involving complex or lengthy documents. Although GRU- and LSTM-based Siamese networks have also shown efficacy in semantic matching tasks (e.g., question-answer retrieval), the Transformer remains a strong contender due to its parallelization and ability to focus on different parts of the input text simultaneously.  
Strengths and Weaknesses of the Proposed Approach.  
A principal strength of our framework lies in its modular design: a pretrained multilingual Sentence Transformer base is combined with a specialized sequential head, allowing for flexible adaptation to different languages and domain-specific text structures. Additionally, the triplet loss function provides a robust way to enforce margin separation between positive and negative pairs. This helps the model to learn a more discriminative embedding space, which is crucial for ranking candidates accurately.  
One potential weakness is the reliance on large-scale pretrained embeddings, which can introduce biases or inaccuracies if the domain-specific vocabulary is underrepresented in the base model. Furthermore, while our attention-based modules capture nuanced textual relationships, they can sometimes be sensitive to hyperparameter tuning (e.g., number of attention heads, hidden size, etc.). This complexity may increase the overhead for practitioners looking for quick deployment without extensive model optimization.  
Impact of Different Sequential Heads.  
Our comparative study of GRU, LSTM, and Transformer heads underscores the importance of architectural choices in the final stage of representation learning. The GRU's simpler gating mechanism proves effective and computationally efficient for moderate text lengths. LSTM, with its explicit memory cell structure, manages longer text dependencies well but can be slower to train. The Transformer's self-attention not only captures intricate global patterns but also scales effectively for varying document lengths [41]. Depending on real-world constraints such as hardware resources, maximum input size, and the criticality of high recall at large \(K\), recruiters or system integrators might select the most suitable head architecture for their specific use case.