Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in Chinese text.While conventional CSC focuses on character substitution errors caused by mistyping, two other common types of character errors, missing and redundant characters, have received less attention.These errors are often excluded from CSC datasets during the annotation process or ignored during evaluation, even when they have been annotated.This issue limits the practicality of the CSC task.To address this issue, we introduce the task of General Chinese Character Error Correction (C2EC), which focuses on all three types of character errors.We construct a high-quality C2EC benchmark by combining and manually verifying data from CCTC and Lemon datasets.We extend the training-free prompt-free CSC method to C2EC by using Levenshtein distance for handling length changes and leveraging an additional prompt-based large language model (LLM) to improve performance.Experiments show that our method enables a 14B-parameter LLM to be on par with models nearly 50 times larger on both conventional CSC and C2EC tasks, without any fine-tuning.

Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

This work proposes a simple training-free prompt-free approach to leverage large language models (LLMs) for the Chinese spelling correction (CSC) task, which is totally different from all previous CSC approaches.The key idea is to use an LLM as a pure language model in a conventional manner.The LLM goes through the input sentence from the beginning, and at each inference step, produces a distribution over its vocabulary for deciding the next token, given a partial sentence.To ensure that the output sentence remains faithful to the input sentence, we design a minimal distortion model that utilizes pronunciation or shape similarities between the original and replaced characters.Furthermore, we propose two useful reward strategies to address practical challenges specific to the CSC task.Experiments on five public datasets demonstrate that our approach significantly improves LLM performance, enabling them to compete with state-of-the-art domain-general CSC models.

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.

Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer "who expressed what kind of sentiment towards what?".Due to the scarcity of labeled data, ORL remains challenging for data-driven methods.In this work, we try to enhance neural ORL models with syntactic knowledge by comparing and integrating different representations.We also propose dependency graph convolutional networks (DEPGCN) to encode parser information at different processing levels.In order to compensate for parser inaccuracy and reduce error propagation, we introduce multi-task learning (MTL) to train the parser and the ORL model simultaneously.We verify our methods on the benchmark MPQA corpus.The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9.29 over the syntaxagnostic baseline.In addition, we find that the contributions from syntactic knowledge do not fully overlap with contextualized word representations (BERT).Our best model achieves 4.34 higher F1 score than the current state-ofthe-art.

Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing accuracy.However, previous work mainly focuses on unsupervised treebank conversion and makes little progress due to the lack of manually labeled data where each sentence has two syntactic trees complying with two different guidelines at the same time, referred as bi-tree aligned data.In this work, we for the first time propose the task of supervised treebank conversion.First, we manually construct a bi-tree aligned dataset containing over ten thousand sentences.Then, we propose two simple yet effective treebank conversion approaches (pattern embedding and treeLSTM) based on the state-of-the-art deep biaffine parser.Experimental results show that 1) the two approaches achieve comparable conversion accuracy, and 2) treebank conversion is superior to the widely used multi-task learning framework in multiple treebank exploitation and leads to significantly higher parsing accuracy.* The first two (student) authors make equal contributions to this work.Zhenghua is the correspondence author.