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. 1 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.

Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored.In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task.Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead.We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders.For the model training, we explore multi-task learning to combine entity labeling and relation extraction tasks; and for the evaluation, we conduct experiments on different datasets with filtering and augmentation.The results demonstrate that our proposed model achieves 65.96% F1 score on the FUNSD dataset.As for the realworld application, our model has been applied to the in-house customs data, achieving reliable performance in the production setting.

Zuyi Bao, Rui Huang, Chen Li, Kenny Zhu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.