Papers by Shijin Wang

Gradient-based Intra-attention Pruning on Pre-trained Language Models (2023.acl-long)

Challenge:	Existing methods rely on entity vector matching, but the purpose of the question is abstract and difficult to match with specific entities. Existing approaches rely only on entity-vector matching, and there is a problem with multi-hop reasoning.
Approach:	They propose a framework that constructs reasoning paths from purposes back to conditions using the KG ontology.
Outcome:	Experiments on the WebQSP and CWQ datasets show that ORT significantly improves the capability of large language models in knowledge graph question answering tasks (KGQA).

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Ziqing Yang, Yiming Cui, Xin Yao, Shijin Wang

CPT-Agent: A Cognitive Process Theory-driven Framework for Student Simulation in Writing Development (2026.acl-long)

Challenge:	Pre-trained language models are computationally expensive and slow in inference due to their large sizes.
Approach:	They propose a structured pruning method which combines pruning with knowledge distillation to yield highly effective models.
Outcome:	The proposed method outperforms other pruning methods in sparsity regimes while maintaining 93% 99% performance.

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Yuhan Chen, Zizhuo Shen, Miaomiao Cheng, Xu Han, Jiefu Gong, Shijin Wang, Wei Song

Linguistics & Cognitive NLP

Challenge:	Existing LLMs model overly capable learners who over-apply feedback, resulting in pedagogically implausible behavior.
Approach:	They propose a framework that decouples cognitive ability from writing proficiency and models their interaction during writing and revision.
Outcome:	The proposed model produces distinguishable proficiency levels and is consistent with instructional theories.

IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation (2021.acl-demo)

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Jiefu Gong, Xiao Hu, Wei Song, Ruiji Fu, Zhichao Sheng, Bo Zhu, Shijin Wang, Ting Liu

Enhancing Chain-of-Thought Reasoning via Neuron Activation Differential Analysis (2025.emnlp-main)

Challenge:	Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way.
Approach:	They propose to use Chinese AEA system IFlyEssayAssess to evaluate essays written by native Chinese students from primary and junior schools.
Outcome:	The proposed system provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization.

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Yiru Tang, Kun Zhou, Yingqian Min, Xin Zhao, Jing Sha, Zhichao Sheng, Shijin Wang

Cross-Lingual Machine Reading Comprehension (D19-1)

Challenge:	Existing studies focus on optimizing external components of CoT, but lack internal explanations for the quality of the model's outputs.
Approach:	They propose an efficient method to identify reasoning-critical neurons by analyzing their activation patterns under reasoning chains of varying quality.
Outcome:	The proposed method shows that neurons in the feed-forward layers are critical in the generation of high-quality reasoning chains.

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Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

Machine Reading Comprehension

Challenge:	Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data.
Approach:	They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches .
Outcome:	The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems.

Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)

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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin

IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning (2023.findings-acl)

Challenge:	Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization.
Approach:	They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach.
Outcome:	The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks.

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Zihang Xu, Ziqing Yang, Yiming Cui, Shijin Wang

Adversarial Training for Machine Reading Comprehension with Virtual Embeddings (2021.starsem-1)

Challenge:	Existing systems for logical reasoning have surpassed the average performance of humans in many tasks like SQuAD but there is still a long way to go when it comes to logical reasoning.
Approach:	They propose an InDicator-Oriented Logic Pre-training task which logically strengthens pre-trained models with the help of 6 types of logical indicators and a logicalally rich dataset.
Outcome:	The proposed task achieves state-of-the-art on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC.

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Ziqing Yang, Yiming Cui, Chenglei Si, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

CharBERT: Character-aware Pre-trained Language Model (2020.coling-main)

Challenge:	Neural networks are vulnerable to adversarial examples that have been mixed with certain perturbations.
Approach:	They propose a novel adversarial training method that perturbs the embedding matrix instead of word vectors to differentiate the roles of passages and questions.
Outcome:	The proposed method is effective universally and further improves the performance of MRC tasks.

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Wentao Ma, Yiming Cui, Chenglei Si, Ting Liu, Shijin Wang, Guoping Hu

Sentence Ordering with a Coherence Verifier (2023.findings-acl)

Challenge:	Pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations . but these methods split a word into subword units and make it incomplete and fragile .
Approach:	They propose a character-aware pre-trained language model to tackle OOV problems . they construct contextual word embedding for each token from sequential character representations .
Outcome:	The proposed model improves on the existing models on multiple NLP benchmarks.

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Sainan Jia, Wei Song, Jiefu Gong, Shijin Wang, Ting Liu

Structured Data in NLP

Challenge:	Recent sentence ordering studies can be classified into 2 categories: pair-wise ranking-based and sequence generation-based methods.
Approach:	They propose a sentence ordering method by plugging a coherence verifier into ranking-based and sequence generation-based methods.
Outcome:	The proposed method improves on topological sorting-based and pointer network-based methods with topological and point-based models.

IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis (D19-3)

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Ziyue Wang, Baoxin Wang, Xingyi Duan, Dayong Wu, Shijin Wang, Guoping Hu, Ting Liu

Information Retrieval

Challenge:	Legal Tech is a system that performs legal consulting, multi-way law searching, and legal document analysis using deep contextual representations and various attention mechanisms.
Approach:	They propose a Chinese legal system that performs legal consulting, multi-way law searching, and legal document analysis using deep contextual representations and various attention mechanisms.
Outcome:	The proposed system performs legal consulting, multi-way law searching, and legal document analysis by exploiting techniques such as deep contextual representations and various attention mechanisms.

Dataset for the First Evaluation on Chinese Machine Reading Comprehension (L18-1)

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Yiming Cui, Ting Liu, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu

MMATH: A Multilingual Benchmark for Mathematical Reasoning (2025.findings-emnlp)

Challenge:	Existing reading comprehension datasets are mostly in English .
Approach:	They propose a Chinese reading comprehension dataset to add diversity to existing reading comprehension data . proposed dataset contains cloze-style reading comprehension and user query reading comprehension .
Outcome:	The proposed dataset is based on a Chinese reading comprehension dataset . it includes two types of cloze-style and user query reading comprehension . the proposed dataset hosted the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017)

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Wenyang Luo, Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)

Challenge:	a benchmark for multilingual complex reasoning spans 374 high-quality math problems across 10 typologically diverse languages.
Approach:	They propose a benchmark for multilingual complex reasoning across 10 languages . they show reasoning in English and answering in target languages can enhance performance .
Outcome:	The proposed benchmark demonstrates that models with high-quality reasoning can perform in multiple languages.

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Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)

Challenge:	Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks .
Approach:	They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates .
Outcome:	The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories .

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Wenjie Zheng, Jianfei Yu, Rui Xia, Shijin Wang

TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)

Challenge:	Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes.
Approach:	They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence.
Outcome:	The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset.

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Ziqing Yang, Yiming Cui, Zhipeng Chen, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model (2024.emnlp-demo)

Challenge:	Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference.
Approach:	They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code.
Outcome:	The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters.

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Dayong Wu, Jiaqi Li, Baoxin Wang, Honghong Zhao, Siyuan Xue, Yanjie Yang, Zhijun Chang, Rui Zhang, Li Qian, Bo Wang, Shijin Wang, Zhixiong Zhang, Guoping Hu

A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)

Challenge:	Large language models (LLMs) have shown remarkable achievements across various language tasks.
Approach:	They propose a scientific literature LLM and a knowledge service system based on it . they collect scientific literature and then pre-train it using autoregressive training .
Outcome:	The proposed system provides literature investigation, paper reading, and academic writing functions.

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Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu

Machine Reading Comprehension

Challenge:	Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them.
Approach:	They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets.
Outcome:	The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts.

Is Graph Structure Necessary for Multi-hop Question Answering? (2020.emnlp-main)

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Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

Structured Data in NLP

Challenge:	Existing studies focus on multi-hop question answering across multiple documents or paragraphs.
Approach:	They propose a graph neural network to deal with graph structure in textual multi-hop reasoning . they propose 'self-attention' and propose removing entire graph structure may not hurt the final results .
Outcome:	The proposed model shows that graph-attention or the entire graph structure can be replaced by self-attention . hotpotQA is a widely used benchmark for multi-hop question answering .

Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020.findings-emnlp)

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Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)

Challenge:	Existing pre-trained language models have shown tremendous improvements across various NLP tasks.
Approach:	They propose to revisit Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pretrained model series to the community.
Outcome:	The proposed model improves on RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac).

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Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

Chart2Code53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation (2025.emnlp-main)

Challenge:	Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs.
Approach:	They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn.
Outcome:	The proposed framework improves strategy selection preferences at each dialogue turn.

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Tianhao Niu, Yiming Cui, Baoxin Wang, Xiao Xu, Xin Yao, Qingfu Zhu, Dayong Wu, Shijin Wang, Wanxiang Che

Programming Languages in NLP

Challenge:	Existing Chart2code-related training datasets suffer from limited scale, limited type coverage, and inadequate complexity.
Approach:	They propose to synthesize chart2code-related training datasets using web plotting code and chart images to address these challenges.
Outcome:	The proposed dataset exhibits the greatest diversity and higher complexity compared to other open-source Chart2code related datasets.

From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation (2025.acl-long)

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Cheng Cheng, Zhenya Huang, GuanHao Zhao, Yuxiang Guo, Xin Lin, Jinze Wu, Xin Li, Shijin Wang

Text Generation

Challenge:	Traditional generation methods focus primarily on textual quality, but they fail to meet complex, multifaceted educational requirements.
Approach:	They propose a method for automatic generating high-quality mathematical problems that align with educational objectives using a dataset of 16k mathematical questions with multi-dimensional educational objectives.
Outcome:	The proposed method improves generating high-quality mathematical questions that meet multi-dimensional educational objectives.

Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (2020.coling-main)

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Zhiqiang Guo, Zhaoci Liu, Zhenhua Ling, Shijin Wang, Lingjing Jin, Yunxia Li

Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network (2022.acl-long)

Challenge:	Existing methods for AD detection are too expensive and time-consuming to cover all potential patients.
Approach:	They propose a contrastive learning method to obtain effective text representations based on monolingual embeddings of BERT and a cross-lingual data augmentation method by building autoencoders to learn the text representation shared by both languages.
Outcome:	The proposed method outperforms other methods on a Mandarin AD corpus and achieves 81.6% detection accuracy.

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Zheng Gong, Kun Zhou, Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

Benchmarking Robustness of Machine Reading Comprehension Models (2021.findings-acl)

Challenge:	a fundamental challenge in modeling math problems is how to fuse semantics of textual description and formulas.
Approach:	They propose a method to continually pre-train language models for improving understanding of math problems with syntax-aware memory networks.
Outcome:	The proposed approach outperforms competitive baselines on four math tasks.

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Chenglei Si, Ziqing Yang, Yiming Cui, Wentao Ma, Ting Liu, Shijin Wang

Robustness in NLP

Challenge:	Existing benchmarks only evaluate models' robustness under test-time perturbations or adversarial attacks.
Approach:	They propose a model-agnostic benchmark to evaluate models' robustness under adversarial attacks.
Outcome:	The proposed model-agnostic benchmark evaluates models under four different types of adversarial attacks.

Chinese Metaphorical Relation Extraction: Dataset and Models (2023.findings-emnlp)

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Guihua Chen, Tiantian Wu, MiaoMiao Cheng, Xu Han, Jiefu Gong, Shijin Wang, Wei Song

Information Extraction & Text Mining

Challenge:	Metaphor identification is a core task in metaphor processing, which involves recognizing and analyzing metaphorical expressions in text.
Approach:	They propose a new formulation of metaphor identification as a relation extraction problem . they use a dataset to analyze metaphorical relations between two spans, a target and a source .
Outcome:	The proposed model can capture the properties of the target and source in Chinese sentences.

Conversational Word Embedding for Retrieval-Based Dialog System (2020.acl-main)

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Wentao Ma, Yiming Cui, Ting Liu, Dong Wang, Shijin Wang, Guoping Hu

Natural Language Interfaces

Challenge:	Existing word embedding methods for retrieval-based dialog systems are based on co-occurrence statistics and train them based upon the same co-existence statistics.
Approach:	They propose a conversational word embedding method which uses the conversation pairs post, reply, and 'reply' they introduce a word alignment model from statistical machine translation and train it on word-level and sentence-level.
Outcome:	The proposed method improves the quality of the selected response on retrieval-based dialog systems.

Dynamic Connected Networks for Chinese Spelling Check (2021.findings-acl)

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Baoxin Wang, Wanxiang Che, Dayong Wu, Shijin Wang, Guoping Hu, Ting Liu

Optimizing Chinese Lexical Simplification Across Word Types: A Hybrid Approach (2024.emnlp-main)

Challenge:	Chinese spelling check (CSC) is a task to detect and correct spelling errors in Chinese text.
Approach:	They propose a new architecture which generates Chinese characters via a Pinyin Enhanced Candidate Generator and then utilizes an attention-based network to model the dependencies between two adjacent Chinese characters.
Outcome:	The proposed method achieves state-of-the-art performance on three human-annotated datasets.

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ZiHao Xiao, Jiefu Gong, Shijin Wang, Wei Song