Papers by Wanxiang Che

Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts (2024.emnlp-main)

Challenge:	Existing multi-hop retrieval of open-domain text-to-SQL tasks is not applicable due to the tendency to retrieve tables similar to those already retrieved but irrelevant to the question.
Approach:	They propose a multi-hop table retrieval with removal task to retrieve unretrieved tables from open-domain text-to-SQL databases.
Outcome:	The proposed method improves performance 5.7% over the previous state-of-the-art methods on open-domain text-to-SQL datasets.

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Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

DAC: Decomposed Automation Correction for Text-to-SQL (2025.findings-emnlp)

Challenge:	Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process.
Approach:	They propose a task and model agnostic approach which harnesses strength and diversity from various languages to achieve better performance across all tasks.
Outcome:	The proposed approach outperforms Python Self-Consistency in almost all tasks and models and achieves comparable or superior performance on ChatGPT.

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network (2020.acl-main)

Challenge:	Existing methods to improve text-to-SQL performance are hard to detect errors in SQL directly.
Approach:	They propose to use decomposed correction to improve text-to-SQL performance . they first detect errors based on decompose subtasks, then use it to correct them .
Outcome:	The proposed method improves text-to-SQL performance by 1.4% compared with previous methods .

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Yangming Li, Kaisheng Yao, Libo Qin, Wanxiang Che, Xiaolong Li, Ting Liu

HuoziIME: An On-Device LLM-Enhanced Input Method for Deep Personalization (2026.acl-demo)

Challenge:	Existing approaches to natural language generation are prone to errors, such as neglecting input slot values and generating redundant slot values.
Approach:	They propose an iterative rectification network to improve general NLG systems . they apply bootstrapping algorithms to sample training candidates and incorporate reward .
Outcome:	The proposed methods significantly reduce the slot error rate for strong baselines.

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Baocai Shan, Yuzhuang Xu, Wanxiang Che

Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)

Challenge:	Mobile input method editors (IMEs) are the primary interface for text input, yet they are constrained to manual typing and struggle to produce personalized text.
Approach:	They propose a personalized on-device IME powered by large language models . they endow HUOZIIME with initial human-like prediction ability .
Outcome:	The proposed IME has initial human-like prediction ability and is optimized for on-device deployment.

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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)

Challenge:	Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation.
Approach:	They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task.
Outcome:	The proposed model improves inference speed by 2.3-2.7x times without compromising model performance.

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Libo Qin, Yijia Liu, Wanxiang Che, Haoyang Wen, Yangming Li, Ting Liu

CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers (2022.coling-1)

Challenge:	Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other.
Approach:	They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history .
Outcome:	The proposed framework outperforms baseline models and produces entity-consistent responses.

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Baoxin Wang, Xingyi Duan, Dayong Wu, Wanxiang Che, Zhigang Chen, Guoping Hu

Syntactic Text Processing

Challenge:	Chinese text correction datasets focus on detecting and correcting Chinese spelling errors and grammatical errors.
Approach:	They propose a Chinese text correction dataset for native speakers . they manually annotated 1,500 Chinese texts written by native speakers.
Outcome:	The proposed dataset can detect and correct Chinese spelling errors and grammatical errors.

Allocating Large Vocabulary Capacity for Cross-Lingual Language Model Pre-Training (2021.emnlp-main)

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Bo Zheng, Li Dong, Shaohan Huang, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei

Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval (2025.acl-demo)

Challenge:	Existing models require a more expressive vocabulary to represent all languages . however, increasing the vocabulary size significantly slows down the pre-training speed .
Approach:	They propose an algorithm VoCap to determine the desired vocabulary capacity of each language.
Outcome:	The proposed algorithm improves cross-lingual model pre-training while reducing side effects of increasing vocabulary size.

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Keyan Xu, Dingzirui Wang, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation (2020.acl-main)

Challenge:	Existing text-to-SQL systems often lack retrieval capabilities for open-domain databases, requiring users to manually filter relevant databases.
Approach:	They propose to use database retrieval technology to locate the required databases in an open-domain database environment and enhance system cross-domain transferability through data augmentation methods.
Outcome:	The proposed system performs excellently in multi-turn text-to-SQL tasks, validating the proposed approach’s effectiveness.

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Jun Xu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

Cross-Lingual Machine Reading Comprehension (D19-1)

Challenge:	Existing word-level policy models that learn dialog policy and language generation from dialog corpora often lead to degeneration issues where the utterances become ungrammatical or repetitive.
Approach:	They propose to represent prior dialog transitions as a graph and learn a CG grounded dialog policy that can foster a more coherent and controllable dialog.
Outcome:	The proposed framework is able to learn dialog policy in open-domain multi-turn conversation.

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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.

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes (2024.acl-long)

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (D19-1)

Challenge:	Numerical reasoning is an essential ability for NLP systems to handle numeric information.
Approach:	They propose a numerical reasoning method that generates reliable reasoning processes by decomposing the answer formula and aim to train models to generate the process with synthesized data.
Outcome:	The proposed method improves on all five datasets with an average improvement of 1.8% compared with baselines and gpt-3.5-turbo.

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Libo Qin, Wanxiang Che, Yangming Li, Haoyang Wen, Ting Liu

Sentiment Analysis

Challenge:	Intent detection and slot filling are two main tasks for building a spoken language understanding system.
Approach:	They propose a framework to incorporate intent information into slot filling tasks . they use a joint model with Stack-Propagation to capture intent semantic knowledge .
Outcome:	The proposed model outperforms existing models on two publicly available datasets and outperformed existing models by a large margin.

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling (2021.findings-acl)

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Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu

Simple and Effective Graph-to-Graph Annotation Conversion (2022.coling-1)

Challenge:	Existing few-shot learning methods learn a single task each time with a few examples . but, real-world applications often contain multiple closely related tasks .
Approach:	They propose a few-shot joint learning scheme that captures intent and slot relationships from only a handful of examples and adapts the bridged metric space to specific few- shot domain.
Outcome:	The proposed model outperforms baseline models on two public datasets on intent and slot . the proposed model significantly outperformed baseline models in one and five shots settings.

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Yuxuan Wang, Zhilin Lei, Yuqiu Ji, Wanxiang Che

Structured Data in NLP

Challenge:	Existing work on graph-structured annotation conversions has focused on feature-based models which are not easily applicable to new conversions.
Approach:	They propose two graph-to-graph conversion approaches which use pseudo data and inherit parameters to guide conversions respectively.
Outcome:	The proposed approaches outperform strong baselines with higher conversion score on a graph-structured dataset and other datasets.

A Two-Stage Framework with Self-Supervised Distillation for Cross-Domain Text Classification (2024.lrec-main)

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Yunlong Feng, Bohan Li, Libo Qin, Xiao Xu, Wanxiang Che

ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning (2023.acl-long)

Challenge:	Existing work on cross-domain text classification relies on domain-invariant features or task-agnostic features.
Approach:	They propose a two-stage framework for cross-domain text classification that leverages or reuses rich labeled data from the source domain and unlabeled data in the target domain.
Outcome:	The proposed framework achieves state-of-the-art on a public cross-domain text classification benchmark.

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Xiao Xu, Bei Li, Chenfei Wu, Shao-Yen Tseng, Anahita Bhiwandiwalla, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan

M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought (2024.acl-long)

Challenge:	Two-Tower Vision-Language models suffer from ineffective layer-by-layer utilization of uni-modal representations and cannot flexibly exploit different levels of unil-modal knowledge.
Approach:	They propose a model architecture that gathers and combines the insights of pre-trained uni-modal experts at different levels to facilitate more comprehensive cross-modal alignment and fusion.
Outcome:	The proposed model outperforms baselines with and without Vision-Language Pre-training (VLP) with 4M VLP data.

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Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, Wanxiang Che

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

Challenge:	MCoT requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning.
Approach:	They propose a benchmark to address the challenges of MCoT, and evaluate it using vision large language models.
Outcome:	The proposed benchmark addresses the above challenges and shows that current models still struggle to reason in M3CoT.

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

MetaPrompting: Learning to Learn Better Prompts (2022.coling-1)

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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Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che

Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)

Challenge:	Recent research on prompting moves from discrete tokens based "hard prompts" to continuous "soft prompts", which employ learnable vectors as pseudo prompt tokens and achieve better performance.
Approach:	They propose a generalized soft prompting method that uses model-agnostic meta-learning to find better initialization for soft prompts.
Outcome:	The proposed method improves on three datasets and brings new state-of-the-art performance.

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Shaolei Wang, Zhongyuan Wang, Wanxiang Che, Ting Liu

KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)

Challenge:	Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain.
Approach:	They propose an unsupervised learning paradigm which can work with unlabeled text corpora.
Outcome:	The proposed method performs better than existing supervised systems using word embeddings.

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Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Yunhong Wang

Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)

Challenge:	Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications.
Approach:	They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics .
Outcome:	The proposed model performs best on KwaiChat but is not perfect in this situation.

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Haoyang Wen, Yijia Liu, Wanxiang Che, Libo Qin, Ting Liu

On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)

Challenge:	Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base.
Approach:	They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models.
Outcome:	The proposed framework outperforms baseline models on automatic and human evaluation on a Stanford Multi-turn Multi-domain task-oriented dialogue dataset.

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Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, Ting Liu

Challenge:	Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks.
Approach:	They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments.
Outcome:	The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation.

Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (C18-1)

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Yutai Hou, Yijia Liu, Wanxiang Che, Ting Liu

Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue (2023.findings-emnlp)

Challenge:	Existing work which augments an utterance without considering its relation with other utterrances, however, has failed to improve the language understanding module.
Approach:	They propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data.
Outcome:	The proposed framework achieves 6.38 and 10.04 F-scores on the Airline Travel Information System dataset and a newly created semantic frame annotation on the Stanford Multi-turn, Multi-domain Dialogue Dataset.

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Yuanxing Liu, Weinan Zhang, Yifan Chen, Yuchi Zhang, Haopeng Bai, Fan Feng, Hengbin Cui, Yongbin Li, Wanxiang Che

OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023.acl-demo)

Challenge:	E-commerce pre-sales dialogues elicit user needs and preferences for items . large language models lack domain-specific knowledge for accurate recommendations .
Approach:	They propose two collaboration strategies to integrate CRS and large language models in pre-sales dialogues.
Outcome:	The proposed methods can be very effective in some cases, the authors say .

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Libo Qin, Qiguang Chen, Xiao Xu, Yunlong Feng, Wanxiang Che

Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants’ API Invocation Capabilities (2024.lrec-main)

Challenge:	Spoken Language Understanding (SLU) is a task-oriented dialogue system . open-source toolkit provides a unified, modularized, and extensible toolkit for SLU .
Approach:	They introduce an open-source toolkit to provide a unified toolkit for spoken language understanding.
Outcome:	The proposed toolkit unifies 10 models for both single-intent and multi-intention scenarios.

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Honglin Mu, Yang Xu, Yunlong Feng, Xiaofeng Han, Yitong Li, Yutai Hou, Wanxiang Che

Challenge:	Existing evaluation methods for human-machine interactions are static and can be misleading.
Approach:	They propose to use a LLM-based user agent to assess an assistant's API call capability without human involvement.
Outcome:	The proposed method mirrors real human conversation patterns in human-machine interactions, and shows that it aligns more closely with human assessment.

Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge (2022.emnlp-main)

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Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou

Discovering Dialog Structure Graph for Coherent Dialog Generation (2021.acl-long)

Challenge:	Existing approaches to text-to-SQL require domain knowledge to parse expert questions into SQL queries.
Approach:	They propose a framework to leverage domain knowledge during parsing by building a new benchmark KnowSQL with domain-specific questions.
Outcome:	The proposed framework improves the performance of the proposed benchmark by 28.2%.

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Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che

Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)

Challenge:	Existing studies on dialog structure graphs from open-domain dialogs have limited number of dialog states and can be laborious and costly to annotate manually.
Approach:	They propose to use dialog structure graph as a model to discover hierarchical latent dialog states and their transitions from corpus to facilitate dialog management in a RL based dialog system.
Outcome:	The proposed model can discover meaningful dialog structure graph and significantly improve multi-turn coherence on two benchmark corpora.

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Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che

Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling (2026.findings-acl)

Challenge:	Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process .
Approach:	They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation.
Outcome:	The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench.

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Shiyu Ji, Yixuan Wang, Yijun Liu, Qingfu Zhu, Wanxiang Che

Challenge:	Large Language Models (LLMs) have demonstrated exceptional performance in reasoning tasks, particularly in mathematics.
Approach:	They propose a dynamic self-consistency framework that integrates System 1 and System 2 reasoning to improve token efficiency and latency.
Outcome:	The proposed method outperforms existing methods, achieving up to 47% reduction in token consumption and 43% reduction in inference latency without significant performance loss.

Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog (2020.acl-main)

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Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, Ting Liu

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

Challenge:	Recent studies show remarkable success in end-to-end task-oriented dialog systems . however, most models rely on large training data, which is difficult to scalable for new domains with limited labeled data.
Approach:	They propose a shared-private network which exploits the relevance between the target domain and each domain.
Outcome:	The proposed model outperforms existing methods on multi-domain dialogue by 13.9% on average.

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

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

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

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)

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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Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che

CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue (2022.coling-1)

Challenge:	Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images.
Approach:	They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images.
Outcome:	The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks.

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Libo Qin, Qiguang Chen, Tianbao Xie, Qian Liu, Shijue Huang, Wanxiang Che, Zhou Yu

Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards (2026.acl-long)

Challenge:	Consistency identification in task-oriented dialog usually consists of three subtasks . a proposed model for consistency identification in dialog is based on an explicit interaction paradigm .
Approach:	They propose a cycle guided interactive learning model that makes information exchange explicit from all the three tasks.
Outcome:	The proposed model achieves state-of-the-art performance pushing the overall score to 56.3% (5.0% point absolute improvement)

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Tianhao Niu, Ziyu Han, Qiguang Chen, Shiqi Zhou, Baocai Shan, Hengjie Fang, Qingfu Zhu, Wanxiang Che

Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)

Challenge:	Existing efforts to generate static visualizations focus on static charts and interactive dashboards.
Approach:	They propose a dashboard2code task that requires a model to explore an interactive dashboard, acquire feedback from its own interactions and generate code that reproduces the target dashboard.
Outcome:	The proposed task is based on 180 carefully designed and manually verified dashboard–code pairs spanning three difficulty levels and covering eight common real-world interaction patterns.

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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che

N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)

Challenge:	Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths.
Approach:	They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models.
Outcome:	The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio.

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Wanxiang Che, Yunlong Feng, Libo Qin, Ting Liu

ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing tools that teach an independent model for each task are not supported in Chinese.
Approach:	They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp .
Outcome:	The proposed platform supports six Chinese NLP tasks.

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Hao Chen, Yukun Yan, Sen Mei, Wanxiang Che, Zhenghao Liu, Qi Shi, Xinze Li, Yuchun Fan, Pengcheng Huang, Qiushi Xiong, Zhiyuan Liu, Maosong Sun

LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)

Challenge:	Existing RAG systems often underutilize the retrieved documents, authors say . they fail to extract and integrate key clues needed to support faithful and interpretable reasoning .
Approach:	a new framework extracts key clues from retrieved content and generates multiple reasoning paths . the framework optimizes the model by selecting the most appropriate reasoning path .
Outcome:	Experiments show that ClueAnchor outperforms baseline RAG frameworks in completeness and robustness.

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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)

Challenge:	Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks.
Approach:	They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework.
Outcome:	The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks.

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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che

Improving Grammatical Error Correction via Contextual Data Augmentation (2024.findings-acl)

Challenge:	Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits.
Approach:	They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task.
Outcome:	The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists.

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Yixuan Wang, Baoxin Wang, Yijun Liu, Qingfu Zhu, Dayong Wu, Wanxiang Che

A Survey on Natural Language Processing for Programming (2024.lrec-main)

Challenge:	Increasing use of synthetic data due to inconsistent error distribution and noisy labels is limiting the use of these data.
Approach:	They propose a method for augmentation of synthetic data with a more consistent error distribution.
Outcome:	The proposed method outperforms strong baselines and achieves state-of-the-art with only a few synthetic data.

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Qingfu Zhu, Xianzhen Luo, Fang Liu, Cuiyun Gao, Wanxiang Che

Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement (2024.findings-emnlp)

Challenge:	Natural language processing for programming is a field of NLP and software engineering . it is used to assist programming, and is increasingly prevalent for its effectiveness in improving productivity.
Approach:	They propose to use NLP techniques to assist programming by obtaining a structure-based representation and a functionality-oriented algorithm.
Outcome:	The proposed approach could relieve developers from laborious work while improving efficiency for non-professional users.

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Yunlong Feng, Dechuan Teng, Yang Xu, Honglin Mu, Xiao Xu, Libo Qin, Qingfu Zhu, Wanxiang Che

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

Challenge:	Decompilation is the process of converting compiled code back into a high-level programming language for analysis when source code is unavailable.
Approach:	They propose two methods to improve decompilation performance without fine-tuning and fine-grained alignment enhancement to achieve further improvements.
Outcome:	The proposed methods achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%.

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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.

A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)

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Yuxuan Wang, Wanxiang Che, Ivan Titov, Shay B. Cohen, Zhilin Lei, Ting Liu

Robustness in NLP

Challenge:	Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans.
Approach:	They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations.
Outcome:	The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling.

Towards Conversational Recommendation over Multi-Type Dialogs (2020.acl-main)

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Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models (2026.acl-long)

Challenge:	In recent years, there has been a significant increase in the work of conversational recommendation due to the rise of voice-based bots.
Approach:	They use a Chinese dialog dataset DuRecDial to study conversational recommendation in the context of multi-type dialogs where bots can proactively lead a conversation from a non-recommendation dialog to a recommendation dialog.
Outcome:	The proposed dataset allows to investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how interact with users for recommendation.

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Xuan Dong, Zhe Han, Tianhao Niu, Qingfu Zhu, Wanxiang Che

Robustness in NLP

Challenge:	Vision-Language-Action models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understood.
Approach:	They propose a step-wise inference-time intervention that aligns representations according to step language sensitivity, significantly improving performance under linguistic variation.
Outcome:	The proposed model significantly improves performance under linguistic variation under non-English instructions under language-agnostic steps.

DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)

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Dengyun Peng, Yuhang Zhou, Qiguang Chen, JinHao Liu, Jingjing Chen, Libo Qin, Wanxiang Che

Scaling Laws for Code: A More Data-Hungry Regime (2026.acl-long)

Challenge:	Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization.
Approach:	They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient optimization.
Outcome:	The proposed methods integrate deep learning paradigms into text-based gradient optimization.

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Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, Rongyi Zhang, Houyi Li, Siming Huang, YuanTao Fan, Wanxiang Che

Challenge:	Code Large Language Models (LLMs) are revolutionizing software engineering, but scaling laws are primarily analyzed on Natural Language (NL).
Approach:	They fit Chinchilla law and Farsser law to test scaling laws for code . they find code is more data-hungry and requires higher data-to-parameter ratio .
Outcome:	The proposed scaling laws show that the more expressive Farsser law offers greater accuracy and scales with model size.

Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)

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Libo Qin, Qiguang Chen, Fuxuan Wei, Shijue Huang, Wanxiang Che

An AMR Aligner Tuned by Transition-based Parser (D18-1)

Challenge:	Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development.
Approach:	They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages.
Outcome:	The proposed method outperforms existing prompting methods on several benchmarks.

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Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, Ting Liu

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)

Challenge:	Experimental results show that our AMR aligner outperforms the rule-based aligner by achieving higher alignment F1 score and consistently improving two open-source AMR parsers.
Approach:	They propose a rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.
Outcome:	The proposed AMR aligner outperforms the current state-of-the-art parser by achieving higher alignment F1 score and consistently improving two open-source AMR parsers.

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Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)

Challenge:	End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity.
Approach:	They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends.
Outcome:	The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.

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Libo Qin, Shijue Huang, Qiguang Chen, Chenran Cai, Yudi Zhang, Bin Liang, Wanxiang Che, Ruifeng Xu

RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals (2025.emnlp-main)

Challenge:	Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection .
Approach:	They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples.
Outcome:	The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection.

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Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che

Improving Language Model Reasoning with Self-motivated Learning (2024.lrec-main)

Challenge:	Recent advances in reasoning large language models (RLLMs) have significantly enhanced reasoning capabilities, leading to brilliant performance on table reasoning.
Approach:	They propose a method which performs iterative row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal.
Outcome:	Experiments show that the proposed method outperforms RLLMs on WikiTableQuestions and TableBench by 4.3% and achieves state-of-the-art results with comparable models.

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Yunlong Feng, Yang Xu, Libo Qin, Yasheng Wang, Wanxiang Che

ArcLight: A Lightweight LLM Inference Architecture for Many-Core CPUs (2026.acl-demo)

Challenge:	Large-scale high-quality training data is important for improving the performance of models.
Approach:	They propose a framework that motivates the model to automatically generate rationales on existing datasets and improves the performance of reasoning through reinforcement learning.
Outcome:	The proposed model outperforms InstructGPT on multiple reasoning datasets and outperformed InstructGPT on other datasets.

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Yuzhuang Xu, Xu Han, Yuxuan Li, Wanxiang Che

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

Challenge:	Existing frameworks for large language model (LLM) inference on CPUs overlook overhead of cross-NUMA memory access.
Approach:	They propose a lightweight LLM inference architecture designed from the ground up for many-core CPUs.
Outcome:	Experimental results show that ArcLight surpasses the performance ceiling of mainstream frameworks, achieving up to 46% higher inference throughput.

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

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

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

Distilling Knowledge for Search-based Structured Prediction (P18-1)

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.

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Yijia Liu, Wanxiang Che, Huaipeng Zhao, Bing Qin, Ting Liu

LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction (2024.lrec-main)

Challenge:	Existing studies have focused on the performance of structured prediction models, but they are often limited by the ambiguities of the reference policy.
Approach:	They propose to distill an ensemble of multiple models trained with different initializations into a single model and use it to explore the search space.
Outcome:	The proposed model outperforms the greedy models on two typical search-based structured prediction tasks and achieves 1.32 in LAS and 2.65 in BLEU over strong baselines.

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Yixuan Wang, Baoxin Wang, Yijun Liu, Dayong Wu, Wanxiang Che

Text Generation

Challenge:	Recent work using model ensemble methods based on voting can effectively mitigate over-correction and improve the precision of the GEC system.
Approach:	They propose a rewriting model that can directly modify the over-correction of GEC system outputs without a model ensemble.
Outcome:	The proposed model can mitigate over-correction and improve accuracy of Chinese grammatical error correction tasks without a model ensemble.

CLIPText: A New Paradigm for Zero-shot Text Classification (2023.findings-acl)

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Libo Qin, Weiyun Wang, Qiguang Chen, Wanxiang Che

Information Extraction & Text Mining

Challenge:	Experimental results show that CLIP can be applied to zero-shot text classification tasks.
Approach:	They propose a CLIP model for zero-shot text classification that integrates prompt into CLIPText to better derive knowledge from CLIP.
Outcome:	The proposed model can be applied to a text-image matching problem and show that it can be used for language tasks.

DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation (2021.emnlp-main)

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Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che

GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling (2021.acl-long)

Challenge:	Existing datasets for conversational recommendation are limited to English and Chinese .
Approach:	They propose a bilingual parallel human-to-human recommendation dialog dataset . the data item is annotated in two languages, both English and Chinese .
Outcome:	The proposed dataset provides a testbed for future studies of multilingual and cross-lingual conversational recommendation.

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Libo Qin, Fuxuan Wei, Tianbao Xie, Xiao Xu, Wanxiang Che, Ting Liu

Sentiment Analysis

Challenge:	Existing joint models for multi-intent SLU only consider intent detection while ignoring slot filling task.
Approach:	They propose a non-autoregressive model for joint multiple intent detection and slot filling . their framework is 11.5 times faster than existing joint models .
Outcome:	The proposed model is 11.5 times faster than existing models and is faster than current models.

Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020.emnlp-main)

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Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, Xiangzhan Yu

Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing (D19-1)

Challenge:	Existing methods to fine-tune deep pretrained language models face catastrophic forgetting problems.
Approach:	They propose a recall and learn mechanism which integrates pretraining and downstream tasks into a single mechanism.
Outcome:	The proposed method achieves state-of-the-art performance on the GLUE benchmark and better average performance than directly fine-tuning of BERT-large.

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Yuxuan Wang, Wanxiang Che, Jiang Guo, Yijia Liu, Ting Liu

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code (2026.findings-acl)

Challenge:	Existing approaches to learn cross-lingual word embeddings in a contextual space are lacking.
Approach:	They propose a method to generate cross-lingual contextualized word embeddings using pre-trained BERT models by learning a linear transformation from contextual word alignments.
Outcome:	The proposed approach outperforms state-of-the-art models on zero-shot cross-lingual transfer parsing and is highly competitive with existing models.

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ShangZhan Li, Xinyu Yin, Xuanyu Jin, Ye He, Yuxin Zhou, Yuxuan Li, Xu Han, Wanxiang Che, Qi Shi, Ting Liu, Maosong Sun

Exploring Hybrid Question Answering via Program-based Prompting (2024.acl-long)

Challenge:	Current development practices face a dichotomy between automation and performance.
Approach:	They propose a framework to empower LLMs with the capability of automated explicit vectorization.
Outcome:	The proposed framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench.

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Qi Shi, Han Cui, Haofeng Wang, Qingfu Zhu, Wanxiang Che, Ting Liu

AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought (2024.findings-acl)

Challenge:	Existing approaches to question answering over heterogeneous data are limited due to large scale of information and organic coupling of heterogenous data.
Approach:	They propose a program-based prompting framework for hybrid question answering tasks . it integrates various functions to perform hybrid information-seeking over data .
Outcome:	The proposed framework surpasses baseline systems and achieves the best performance under the fewshot settings.

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Yongheng Zhang, Qiguang Chen, Min Li, Wanxiang Che, Libo Qin

Language Anisotropic Cross-Lingual Model Editing (2023.findings-acl)

Challenge:	Existing approaches to cross-lingual chain-of-thought integrate reasoning knowledge from different languages, but they still rely on manual language specification and weight allocation.
Approach:	They propose an automatic cross-lingual alignment planning framework that integrates reasoning knowledge from different languages.
Outcome:	The proposed framework surpasses existing methods that require manual effort to integrate languages.

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Yang Xu, Yutai Hou, Wanxiang Che, Min Zhang

Cross-Lingual Transfer

Challenge:	Existing work studies monolingual model editing, which lacks cross-lingual transferability to perform editing simultaneously across languages.
Approach:	They propose a framework to naturally adapt monolingual model editing approaches to the cross-lingual scenario using parallel corpus.
Outcome:	The proposed framework adapts monolingual model editing approaches to the cross-lingual scenario using parallel corpus and amplifies different subsets of parameters for each language.

Consistency Regularization for Cross-Lingual Fine-Tuning (2021.acl-long)

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Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang, Zewen Chi, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei

Multilinguality

Challenge:	Experimental results show that consistency regularization improves cross-lingual fine-tuning . pre-trained cross-linguistic models can transfer task-specific supervision from one language to the other .
Approach:	They propose to improve cross-lingual fine-tuning with consistency regularization . they use example consistency regularized to penalize prediction sensitivity to four types of data augmentations .
Outcome:	The proposed method improves cross-lingual fine-tuning across tasks . it can be generalized to other target languages without additional training .

Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (2020.acl-main)

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Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, Ting Liu

TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack (2022.findings-emnlp)

Challenge:	Existing few-shot learning methods for slot tagging are based on similarity-based methods, but they are difficult to apply to an unseen domain due to the discrepancy of label sets.
Approach:	They propose a label-enhanced task-adaptive projection network to transfer abstract label dependency patterns as transition scores into the conditional random field (CRF) Experimental results show that their model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.
Outcome:	The proposed model outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.

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Zhen Yu, Xiaosen Wang, Wanxiang Che, Kun He

Robustness in NLP

Challenge:	Existing textual adversarial attacks use gradient or prediction confidence to generate adversarials, making it hard to be deployed in real-world applications.
Approach:	They propose a textual adversarial attack that randomly perturbs lots of words to craft an adversarial example.
Outcome:	The proposed attack outperforms existing hard-label attacks in terms of attack performance and adversary quality.

Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System (2021.emnlp-main)

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Libo Qin, Tianbao Xie, Shijue Huang, Qiguang Chen, Xiao Xu, Wanxiang Che

Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension (2020.acl-main)

Challenge:	Consistency Identification has been used for preventing inconsistent response generation, but few efforts have been made to task-oriented dialogue.
Approach:	They propose a dataset for Consistency Identification in task-oriented dialog system.
Outcome:	The proposed dataset is based on a single label and provides fine-grained labels to encourage model to know what inconsistent sources lead to it.

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Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, Ting Liu

GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding (2022.acl-long)

Challenge:	Existing approaches to machine reading comprehension treat documents at their hierarchical nature, ignoring their dependencies.
Approach:	They propose a machine reading comprehension benchmark with two-grained answers . they use graph attention networks to model documents at their hierarchical nature .
Outcome:	The proposed framework outperforms existing systems at long and short answer criteria.

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Libo Qin, Qiguang Chen, Tianbao Xie, Qixin Li, Jian-Guang Lou, Wanxiang Che, Min-Yen Kan

Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL (2024.findings-emnlp)

Challenge:	Existing approaches to zero-shot cross-lingual spoken language understanding rely on shared parameters, which can only perform implicit alignment across languages.
Approach:	They propose a global-local contrastive learning framework to achieve a fine-grained cross-lingual transfer . they employ bilingual dictionaries to construct multilingual views of the same utterance .
Outcome:	Experiments on MultiATIS++ show that GL-CLeF achieves the best performance . GL is based on dictionaries and encourages representations to be more similar than negative example pairs .

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling (2020.findings-emnlp)

Challenge:	Existing studies have explored selecting relevant demonstrations from a human-labeled demonstration pool, but these methods lack diversity and incur high labeling costs.
Approach:	They propose a method that iteratively fuses demonstrations to create a diverse demonstration pool based on human labeling or even from scratch with LLMs, reducing labeling costs.
Outcome:	The proposed method achieves an average improvement of 2.1% based on existing labeling and 5.5% from scratch on mainstream datasets.

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Libo Qin, Xiao Xu, Wanxiang Che, Ting Liu

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging (2022.findings-acl)

Challenge:	Existing models focus on the single intent scenario, ignoring the fine-grained multiple intents information integration for token-level slot prediction.
Approach:	They propose an Adaptive Graph-Interactive Framework for joint multiple intent detection and slot filling . they propose an intent-slot graph interaction layer to model the strong correlation between the slot and intents .
Outcome:	The proposed framework improves on three multi-intent datasets and new state-of-the-art performance on single-intention datasets.

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Yutai Hou, Cheng Chen, Xianzhen Luo, Bohan Li, Wanxiang Che

Parsing Tweets into Universal Dependencies (N18-1)

Challenge:	Recent results show that prompting methods are inefficient for slot tagging tasks . inverse prompting only requires a one-turn prediction for each slot type .
Approach:	They propose an inverse prompting paradigm that reversely predicts slot values given slot types . the method is faster and significantly improves the effect on 10-shot setting .
Outcome:	The proposed method improves over 6.1 F1-scores on 10-shot setting and achieves new state-of-the-art performance.

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Yijia Liu, Yi Zhu, Wanxiang Che, Bing Qin, Nathan Schneider, Noah A. Smith

Syntactic Text Processing

Challenge:	a new tweet treebank for English is designed to analyze tweets with universal dependencies (UD).
Approach:	They extend the universal dependencies guidelines to include special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies.
Outcome:	The proposed method outperforms state-of-the-art parsers on other treebanks in accuracy and speed.

Adaptive Unsupervised Self-training for Disfluency Detection (2022.coling-1)

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Zhongyuan Wang, Yixuan Wang, Shaolei Wang, Wanxiang Che

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

Challenge:	Recent studies on disfluency detection heavily relies on human annotations, which are difficult and expensive to obtain in practice.
Approach:	They propose an unsupervised method that reweights the importance of each training example according to its grammatical feature and prediction confidence.
Outcome:	The proposed method improves 2.3 points over the current SOTA unsupervised method and is competitive with the SOTA supervised method.

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

SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models (2024.acl-long)

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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Weixiang Zhao, Shilong Wang, Yulin Hu, Yanyan Zhao, Bing Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency (P19-1)

Challenge:	Existing methods to address catastrophic forgetting and knowledge transfer in large language models (LLMs) ignore potential of aligning the two modules to effectively address catastrophic forgetting and knowledge transfers simultaneously.
Approach:	They propose a Shared Attentive Learning & Selection module to align the PET learning and selection modules to address catastrophic forgetting and knowledge transfer simultaneously.
Outcome:	Experiments on two CL benchmarks show that the proposed framework is superior when scaled to different model sizes, different model architectures and unseen tasks.

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Shuhuai Ren, Yihe Deng, Kun He, Wanxiang Che

Information Retrieval

Challenge:	Existing approaches to attack text classification are limited due to the complexity of the problem.
Approach:	They propose a greedy algorithm to generate adversarial examples that maintain lexical correctness, grammatical correctiness and semantic similarity.
Outcome:	The proposed algorithm maintains lexical correctness, grammatical correctity and semantic similarity well and is hard for humans to perceive.

MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering (2025.findings-emnlp)

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Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che