Thanks to the strong expressive power of neural networks, researchers in natural language processing have increasingly turned their attention to multi-task learning. Most approaches aim to improve performance across multiple tasks by sharing parameters within a neural network, allowing models to learn shared representations that benefit all tasks involved.
This paper presents a joint multi-task model designed to handle complex linguistic tasks through a gradually deepening architecture. Unlike traditional parallel multi-task learning methods, which typically treat all tasks equally and share parameters at the same level, this approach leverages the hierarchical nature of NLP tasks—such as part-of-speech (POS) tagging, chunking, dependency parsing, semantic relatedness, and text entailment—to build a more structured learning framework.
In this model, each task has its own objective function, and the model is trained in a sequential manner based on the task hierarchy. The lower-level tasks provide input for higher-level ones, enabling the model to progressively build more abstract representations. This hierarchical design not only reflects the linguistic structure but also improves the overall effectiveness of the system.
In the field of NLP, tasks often exhibit strong interdependencies. For example, part-of-speech tagging is a foundational step that supports more complex tasks like dependency parsing and semantic analysis. By leveraging these relationships, multi-task learning can enhance the performance of individual tasks by promoting knowledge transfer and shared representation learning.
However, many existing multi-task frameworks fail to account for the hierarchical nature of NLP tasks. To address this limitation, the proposed model introduces a hierarchical growth neural network that explicitly considers the dependencies between different levels of linguistic processing. This allows the model to better capture the flow of information from basic lexical analysis to higher-level semantic understanding.
The overall architecture of the model is built upon this hierarchical principle. Lower layers focus on word-level and syntactic features using bidirectional LSTMs, while higher layers incorporate semantic representations derived from previous tasks. During training, each task is optimized with its own objective function, and the model is trained in a top-down fashion, ensuring that lower-level outputs are effectively used in higher-level predictions.
Additionally, the model incorporates shortcut connections and label embeddings to enhance feature reuse and prevent catastrophic forgetting. A sequential regularization term is also added to the loss function, ensuring that the model retains previously learned knowledge as it progresses through more complex tasks.
Experimental results show that the proposed method outperforms single-task models across all evaluated tasks, including POS, CHUNK, DEP, Relatedness, and Entailment. When compared to mainstream multi-task learning approaches, the model achieves competitive or superior performance, demonstrating the effectiveness of the hierarchical design.
Furthermore, ablation studies reveal the importance of key components such as shortcut connections, label embeddings, and sequential regularization. These elements contribute significantly to the model's ability to maintain stability and performance across multiple tasks.
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