SG-Net: Syntax-Guided Machine Reading Comprehension (1908.05147v3)

Abstract: For machine reading comprehension, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride 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 mechanism 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. To verify its effectiveness, the proposed SG-Net is applied to typical pre-trained LLM BERT which is right based on a Transformer encoder. Extensive experiments on popular benchmarks including SQuAD 2.0 and RACE show that the proposed SG-Net design helps achieve substantial performance improvement over strong baselines.

SG-Net: Syntax-Guided Machine Reading Comprehension

The paper introduces SG-Net, a syntax-guided network designed to enhance machine reading comprehension (MRC) by integrating syntactic constraints into the self-attention mechanism of Transformer-based models. This approach aims to refine linguistic modeling by selectively focusing on syntactically relevant words, thereby reducing noise and enhancing the accuracy of word representations.

Introduction to the Concept

The core proposal involves using syntactic dependencies to guide the attention mechanism within a self-attention network (SAN). This contrasts with traditional attentive models that generally apply attention without explicating constraints, potentially leading to inefficiencies when processing lengthy passages. In SG-Net, the dependency parsing identifies a syntactic dependency of interest (SDOI) for each word, including itself and its ancestor nodes from the syntactic tree. This guides the SG-Net's attention mechanism, enabling it to prioritize linguistically significant interactions over irrelevant ones.

Architecture and Implementation

SG-Net consists of a dual-contextual architecture, combining the syntactic-enhanced self-attention with the conventional SAN structure within Transformer encoders. The architecture integrates two representation formats: the original SAN output and a syntax-guided SAN output derived from parsing dependencies. These representations are aggregated using a dual-context layer, refining the final output for downstream tasks.

SG-Net was implemented with BERT and evaluated on established MRC benchmark datasets, namely SQuAD 2.0, for span-based answer extraction, and RACE, for multiple-choice question answering. In these setups, pre-trained syntactic dependency parsers were employed to provide the necessary syntactic annotations, fundamentally enhancing the model's ability to handle complex, detailed, and lengthy texts accurately.

Experimental Results

In empirical evaluations, SG-Net demonstrated robust improvements over strong baselines. On SQuAD 2.0, SG-Net achieved substantial performance gains in both Exact Match (EM) and F1 scores, surpassing various state-of-the-art models. Similarly, on the RACE dataset, the model exhibited superior accuracy, confirming the efficacy of integrating syntactic guidance in attention mechanisms for MRC tasks.

These improvements highlight SG-Net's ability to maintain accuracy across varying question lengths, particularly outperforming the standard BERT implementation in handling longer questions. This suggests that syntax-guided attention provides significant benefits in parsing and understanding intricate sentence structures, supporting better comprehension and answering capabilities in MRC systems.

Implications and Future Directions

The incorporation of syntactic structures into attention mechanisms signifies a valuable step towards more nuanced and linguistically aware NLP models. The implications extend to various language processing applications beyond simple reading comprehension, offering potential advancements in translation, sentiment analysis, and more complex language understanding tasks.

Future research could explore optimizing syntax-guided frameworks further, finding new applications across different Transformer architectures, and refining syntactic parsers to enhance integration with diverse NLP models. Additionally, the examination of different syntactic features and their impacts on model performance could yield deeper insights into crafting comprehensive language understanding systems.

In conclusion, SG-Net presents a methodical enhancement to prevailing Transformer architectures, showcasing the beneficial role of syntactic guidance in refining machine reading comprehension capabilities.