SG-Net: Syntax Guided Transformer for Language Representation.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Computational Linguistics

Background:

• Effective language representation is crucial for AI, but traditional models struggle with lengthy texts and noise.
• Attention mechanisms in current models can focus on irrelevant words, hindering performance.

Purpose of the Study:

• To enhance linguistic representations by incorporating explicit syntactic constraints into attention mechanisms.
• To develop a novel Syntax-Guided Network (SG-Net) for improved text modeling.

Main Methods:

• Introduced a Syntactic Dependency of Interest (SDOI) design into self-attention networks (SANs) within Transformer encoders.
• Developed a dual-contextual architecture combining the SDOI-SAN with the original SAN to form the SG-Net.
• Applied the SG-Net to standard Transformer encoders.

Main Results:

• The SG-Net demonstrated improved linguistically motivated word representations.
• Syntax-guided attention mechanisms led to more accurate focus on relevant words.
• Experiments showed the effectiveness of SG-Net across various NLP tasks.

Conclusions:

• Integrating syntactic information into attention mechanisms significantly enhances language representation models.
• The proposed SG-Net offers a promising approach for advancing AI's language understanding capabilities.
• SG-Net's effectiveness is validated on benchmark tasks like machine reading comprehension, natural language inference, and neural machine translation.