Dependency-aware self-attention for robust neural machine translation.

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Machine Translation

Background:

• Conventional Transformer models struggle with syntactic dependencies due to position-invariant self-attention.
• This limitation is particularly pronounced in low-resource and morphologically rich language scenarios.
• Effective integration of contextual information is crucial for advancing neural machine translation (NMT).

Purpose of the Study:

• To propose a novel Dependency-Aware Self-Attention (DASA) mechanism for Transformer-based NMT.
• To enhance the explicit incorporation of syntactic dependency structures within the attention computation.
• To improve the structural awareness and representation quality of NMT models.

Main Methods:

• Leveraging a dependency parser to derive syntactic trees and generate a dependency distance matrix.
• Transforming the distance matrix into a normalized syntactic bias.
• Integrating the syntactic bias into the self-attention mechanism via element-wise modulation of attention logits.

Main Results:

• DASA guides attention weights towards syntactically relevant tokens, improving structural awareness.
• Substantial improvements in translation performance were observed, particularly in low-resource settings.
• Enhanced syntactic awareness and robustness were demonstrated, especially under data scarcity.

Conclusions:

• The proposed DASA mechanism effectively addresses the limitations of position-invariant self-attention in NMT.
• Explicitly incorporating syntactic information significantly boosts translation quality, especially in data-scarce environments.
• DASA offers a promising direction for developing more robust and linguistically informed NMT systems.