[Paper Review] SG-Net: Syntax Guided Transformer for Language Representation

Zhuosheng Zhang, Yuwei Wu+4 more|arXiv (Cornell University)|Dec 27, 2020

Topic Modeling4 citations

TL;DR

This paper proposes SG-Net, a syntax-guided Transformer architecture that enhances language representation by integrating syntactic dependency structures into self-attention mechanisms. By introducing a Syntax-Driven Output-Intent (SDOI) mask to guide attention, SG-Net improves focus on linguistically relevant spans, achieving state-of-the-art performance on machine reading comprehension, natural language inference, and neural machine translation tasks with minimal architectural changes.

ABSTRACT

Understanding human language is one of the key themes of artificial intelligence. For language representation, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy texts and getting rid of the noises is essential to improve its performance. Traditional attentive models attend to all words without explicit constraint, which results in inaccurate concentration on some dispensable words. In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanisms for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the SAN from the original Transformer encoder through a dual contextual architecture for better linguistics inspired representation. The proposed SG-Net is applied to typical Transformer encoders. Extensive experiments on popular benchmark tasks, including machine reading comprehension, natural language inference, and neural machine translation show the effectiveness of the proposed SG-Net design.

Motivation & Objective

• To improve the precision of attention mechanisms in Transformer-based models by incorporating syntactic structure as explicit guidance.
• To address the issue of noisy, unstructured attention in long and complex texts by focusing on linguistically relevant word dependencies.
• To develop a lightweight, plug-in solution that enhances pre-trained models like BERT without retraining from scratch.
• To verify the effectiveness and generalization of syntax-guided attention across diverse NLP tasks such as reading comprehension, inference, and translation.

Proposed method

• Introduces Syntax-Driven Output-Intent (SDOI) masks derived from syntactic dependency parsing to constrain self-attention matrices.
• Constructs a dual contextual architecture that fuses original self-attention representations with syntax-guided attention outputs.
• Applies the SDOI mask to modify the attention scores in the self-attention layer, ensuring attention is focused on syntactically relevant word pairs.
• Uses pre-trained BERT as the base encoder and adds an extra layer that applies the SDOI mask to the attention mechanism, enabling plug-in integration.
• Processes syntactic parsing offline, avoiding the need to retrain or modify the core Transformer architecture.
• Employs a dual attention mechanism that preserves both standard and syntax-guided representations for improved contextual understanding.

Experimental results

Research questions

• RQ1 Can syntactic dependency structures improve the accuracy and interpretability of attention mechanisms in Transformer-based models?
• RQ2 How does syntax-guided attention affect performance on downstream NLP tasks such as machine reading comprehension and natural language inference?
• RQ3 Can a lightweight, plug-in module enhance pre-trained models like BERT without architectural changes or retraining?
• RQ4 To what extent does syntax-guided attention reduce attention to irrelevant or noisy words in long, complex sentences?
• RQ5 How robust is the method when using weak or imperfect syntactic parsers?

Key findings

• SG-Net achieves significant improvements on machine reading comprehension benchmarks, outperforming standard BERT and other strong baselines.
• The model shows consistent gains across multiple tasks, including natural language inference and neural machine translation, demonstrating strong generalization.
• The syntax-guided attention mechanism reduces attention to irrelevant words, especially in long and complex input sequences.
• The dual contextual architecture effectively balances original and syntax-informed representations, enhancing model robustness.
• Even with weak syntactic parsers, the dual attention design mitigates error propagation and maintains high performance.
• The method is lightweight and easily adaptable to existing Transformer models, requiring only fine-tuning without retraining from scratch.