SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser

Abstract: This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this idea is that off-the-shelf parsers are unreliable when processing ungrammatical sentences. To confront this challenge, we propose to build a tailored GEC-oriented parser (GOPar) using parallel GEC training data as a pivot. First, we design an extended syntax representation scheme that allows us to represent both grammatical errors and syntax in a unified tree structure. Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences. Finally, we train GOPar with such projected trees. For GEC, we employ the graph convolution network to encode source-side syntactic information produced by GOPar, and fuse them with the outputs of the Transformer encoder. Experiments on mainstream English and Chinese GEC datasets show that our proposed SynGEC approach consistently and substantially outperforms strong baselines and achieves competitive performance. Our code and data are all publicly available at https://github.com/HillZhang1999/SynGEC.

• The paper introduces SynGEC, a novel grammatical error correction system that integrates syntactic information using a tailored GEC-oriented parser (GOPar).
• SynGEC employs an extended dependency syntax tree that explicitly marks grammatical errors and trains its parser on error-free target trees projected onto source sentences.
• Experimental results show that SynGEC achieves substantial improvements on English and Chinese GEC datasets, significantly enhancing performance on context-sensitive errors like tense and agreement.

Analysis of SynGEC: Syntax-Enhanced Grammatical Error Correction

The paper "SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser" presents an innovative approach to grammatical error correction (GEC) which integrates syntactic information into the encoder part of GEC models. The authors address the critical challenges associated with parsing ungrammatical sentences by proposing a tailored GEC-oriented parser (GOPar) which leverages both grammatical errors and syntactic structures within a unified framework. This approach is particularly notable for its systematic treatment of dependency syntactic information, facilitating notable improvements in GEC systems across English and Chinese datasets.

Methodological Framework

The SynGEC system combines a syntactic parsing strategy specifically adapted for error correction tasks. The core methodology involves extending the standard dependency syntax tree to encapsulate grammatical errors, using specialized GED labels—namely "S", "R", and "M"—to denote substituted, redundant, and missing errors respectively. This extended representation builds upon standard approaches, providing a nuanced method for handling non-canonical syntactic structures prevalent in erroneous texts.

Key to this framework is the utilization of parallel GEC training data. The GOPar is trained on trees derived from error-free target sentences, projected onto source sentences containing grammatical errors. This novel approach to parser training bypasses traditional manual annotation whilst accommodating realistic grammatical deviations found in learner data.

Experimental Results

The SynGEC system was rigorously evaluated on multiple English and Chinese GEC datasets. A detailed set of experiments highlight its effectiveness, demonstrating substantial improvements over baseline Transformer models, with introduced syntax contributing 4.4/4.2 F$_{0.5}$ increases on CoNLL-14 and BEA-19 test sets in English, and similar improvement across Chinese datasets such as NLPCC-18. Specifically, the tailored syntax was found to enhance correction performance notably for context-sensitive grammatical aspects like tense, agreement, and punctuation errors, where long-range dependencies play a crucial role.

Theoretical and Practical Implications

From a theoretical standpoint, the paper enriches our understanding of the interplay between GEC tasks and syntactic parsing, revealing the critical importance of syntactic information in refining monolingual translation approaches to error correction. Practically, the SynGEC model holds potential for improved deployment in language learning contexts and automated proofreading tools, providing a more fine-grained correction mechanism driven by syntactic awareness.

Future Directions

The authors articulate several avenues for extending their work. Enhancements to the syntax representation scheme, including more sophisticated labeling strategies to capture complex error types, are one such area. Additionally, broadening the multilingual applicability of SynGEC beyond English and Chinese settings may bolster its utility in global language applications.

Overall, the SynGEC approach represents a significant advancement in the field of grammatical error correction, combining syntactic insight with robust error detection to deliver improved correction fidelity in diverse linguistic contexts. As the methodologies mature, they promise to inform future developments in AI-driven grammatical error processing and beyond.