Papers by Wei Wen

Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction (2025.findings-acl)

Challenge:	Existing knowledge distillation methods rely on a single teacher embedding space . existing methods overlook valuable complementary knowledge from teachers in distinct embeddable spaces.
Approach:	They propose a knowledge distillation framework that leverages dual teachers in embedding spaces to enhance performance.
Outcome:	The proposed framework significantly improves knowledge distillation performance by leveraging dual teachers in distinct embedding spaces.

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Guangyue Peng, Wei Li, Wen Luo, Houfeng Wang

Representation Learning

Challenge:	a novel method for encoding fine-grained error patterns improves performance on GEC.
Approach:	They propose a method for encoding grammatical errors from LLMs' internal states using a GER method.
Outcome:	The proposed method significantly boosts performance in ICL settings on multilingual GEC datasets.

Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction (2025.naacl-long)

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Wei Li, Wen Luo, Guangyue Peng, Houfeng Wang

MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)

Challenge:	Grammatical error correction (GEC) aims to correct grammatical, spelling, and semantic errors in natural language text.
Approach:	They propose a retrieval method based on natural language grammatical error explanations to match inputs with pre-constructed databases where explanations for erroneous samples are generated by LLMs.
Outcome:	The proposed method outperforms existing semantic and BM25-based retrieval techniques without additional training or language adaptation.

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Linhan Zhang, Qian Chen, Wen Wang, Chong Deng, ShiLiang Zhang, Bing Li, Wei Wang, Xin Cao

IterAlign: Iterative Constitutional Alignment of Large Language Models (2024.naacl-long)

Challenge:	Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content.
Approach:	They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document.
Outcome:	The proposed method outperforms state-of-the-art methods on six benchmarks . it achieves average 3.53 improvement over the existing method .

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Xiusi Chen, Hongzhi Wen, Sreyashi Nag, Chen Luo, Qingyu Yin, Ruirui Li, Zheng Li, Wei Wang

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

Challenge:	Empirical results show that iterAlign improves truthfulness, helpfulness, harmlessness and honesty, improving the LLM alignment by up to 13.5% in harmlessness.
Approach:	They propose a data-driven constitution discovery and self-alignment framework called IterAlign to overcome these drawbacks by leveraging red teaming to uncover weaknesses of an LLM.
Outcome:	Empirical results show that iterAlign improves truthfulness, helpfulness, harmlessness and honesty by up to 13.5%.

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

Mitigating Posterior Salience Attenuation in Long-Context LLMs with Positional Contrastive Decoding (2025.acl-short)

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

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Zikai Xiao, Ziyang Wang, Wen Ma, Yan Zhang, Wei Shen, WangYan WangYan, Luqi Gong, Zuozhu Liu

Challenge:	Current solutions incur prohibitive training costs, leaving statistical behaviors and cost-effective approaches underexplored.
Approach:	They propose a positional contrast decoding technique that contrasts long-aware attention with designed local-awn attention.
Outcome:	The proposed model achieves state-of-the-art performance on long-context benchmarks.

FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)

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Xiaoning Dong, Chengyan Wu, Yajie Wen, Yu Chen, Yun Xue, Zhang Jing, Wei Xu, Bolei Ma

Information Retrieval

Challenge:	Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty.
Approach:	They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs.
Outcome:	Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs).

Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective (2025.findings-acl)

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Yuchen Wen, Keping Bi, Wei Chen, Jiafeng Guo, Xueqi Cheng

Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)

Challenge:	Existing studies have shown that large language models (LLMs) can elicit implicit biases that hurt certain demographics without explicit harmful words.
Approach:	They propose three attack approaches to elicit agreements to biased viewpoints from LLMs from a psychometric perspective and built two benchmarks to compare them.
Outcome:	The proposed methods elicit agreements to biased viewpoints more effectively than baselines.

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Kun Zhou, Wayne Xin Zhao, Sirui Wang, Fuzheng Zhang, Wei Wu, Ji-Rong Wen

Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement (2026.acl-long)

Challenge:	Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks.
Approach:	They propose a framework for fine-tuning PLMs using a masked language model and Gaussian noise to augment semantically relevant examples with sufficient diversity.
Outcome:	The proposed framework improves the robustness of pre-trained language models and alleviates performance degradation under adversarial attacks.

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Zhenxin Qin, Qiang Li, Qingzhuo Wang, Ruiyang Qin, Zhihua Wei, Wen Shen

Multimodality

Challenge:	Existing research has focused on mitigating object hallucinations but often overlooks more complex relation hallucines, especially action relations involving interactions between objects.
Approach:	They propose a framework to locate action-relevant image regions and enhance the LVLM’s attention to those regions by using a Relation-aware Visual Enhancement method.
Outcome:	The proposed method achieves superior performance in mitigating action-relation hallucinations with negligible additional inference cost.

Odysseus Navigates the Sirens’ Song: Dynamic Focus Decoding for Factual and Diverse Open-Ended Text Generation (2025.acl-long)

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Wen Luo, Feifan Song, Wei Li, Guangyue Peng, Shaohang Wei, Houfeng Wang

Text Generation

Challenge:	Existing decoding methods struggle to balance factuality and diversity . Deterministic decoding approaches suffer from degeneration and lack of diversity - a problem that is not addressed by the current literature.
Approach:	They propose a plug-and-play stochastic approach that adjusts decoding focus based on distributional differences across layers, leveraging the modular nature of factual knowledge within LLMs.
Outcome:	Extensive experiments on seven datasets show that DFD significantly improves performance.

Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model (2024.findings-acl)

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Zhiwei Li, Ran Song, Caihong Sun, Wei Xu, Zhengtao Yu, Ji-Rong Wen

Breaking the Evaluation Paradox: Evaluating High-Entropy Search with Computationally Irreducible Constraints (2026.findings-acl)

Challenge:	Existing factor mining models are inefficient and inefficient, resulting in a significant challenge to extract interpretable factors.
Approach:	They propose a model that integrates the strengths of both neural and symbolic models for factor mining.
Outcome:	The proposed model surpasses the SOTA RankIC and RankICIR in predicting S&P 500 returns on real-world stock market data.

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Juntao Wu, Wei Wen, Xianting Huang, Shuai Pang, Ruizhi Qiao, Xing Sun, Ke Wang

Information Retrieval

Challenge:	a new framework for evaluation of exhaustive search capabilities is needed . high-entropy enumeration tasks make such ground truth impossible for humans to create . VERITAS is a framework built on the principle of computationally irreducible constraints .
Approach:	They propose a framework that uses non-optimizable constraints to create verifiable searches . VERITAS can generate infinite number of test cases with perfect ground truth and precise difficulty control .
Outcome:	a new evaluation framework for large language models is based on non-optimizable constraints . the framework can generate infinite number of test cases with perfect ground truth and precise difficulty control .

Embracing Large Language Models in Traffic Flow Forecasting (2025.findings-acl)

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Yusheng Zhao, Xiao Luo, Haomin Wen, Zhiping Xiao, Wei Ju, Ming Zhang

DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy (2024.acl-long)

Challenge:	Existing methods to predict future traffic flows capture spatio-temporal dependencies, but they fail to adapt to test-time environmental changes.
Approach:	They propose to use large language models to help traffic flow forecasting by capturing spatio-temporal dependencies and using a large language model to select the most likely result.
Outcome:	The proposed method is based on large language models (LLMs) and an LLM-based selector.

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Hongda Sun, Weikai Xu, Wei Liu, Jian Luan, Bin Wang, Shuo Shang, Ji-Rong Wen, Rui Yan

Reasoning

Challenge:	Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks.
Approach:	They propose a new approach that rethinks the reasoning process as an evolution from indeterminacy to determinacy.
Outcome:	The proposed model surpasses all baselines on various logical reasoning benchmarks.

Bloom-Eval: A Hierarchical Evaluation Benchmark for Automatic Survey Generation Based on Bloom’s Taxonomy (2026.acl-long)

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Fei Zhang, Zhe Zhao, HaiBin Wen, Tianshuo Wei, Zaixi Zhang, Chao Yang, Ye Wei

Text Generation

Challenge:	Existing evaluation methods suffer from cognitive dimensional simplification and methodological unreliability due to the ”LLM-as-a-Judge” approach.
Approach:	They propose a six-tiered benchmark that evaluates ASG systems by prioritizing deterministic algorithms and introducing a GRADE approach for abstract abilities.
Outcome:	The proposed method provides the ASG field with a systematic, reproducible, and theoretically grounded benchmark to guide future research.

Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)

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Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo

Question Answering

Challenge:	Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model.
Approach:	They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering.
Outcome:	The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies.

FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation (2025.acl-long)

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Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li

Programming Languages in NLP

Challenge:	Existing benchmarks focus on standalone programming problems, such as HumanEval, MBPP, and LiveCodeBench.
Approach:	They propose to use large language models to evaluate their ability to perform incremental development within code repositories by collecting pull requests from 83 GitHub repositorias and using rule-based and intent-based filtering to construct task instances focused on new feature development.
Outcome:	The proposed benchmarks show that large language models perform significantly worse in the FEA-Bench, highlighting considerable challenges in repository-level incremental code development.

Personalized Topic Selection Model for Topic-Grounded Dialogue (2024.findings-acl)

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Shixuan Fan, Wei Wei, Xiaofei Wen, Xian-Ling Mao, Jixiong Chen, Dangyang Chen

Dialogue Systems & Conversational Agents

Challenge:	Existing topic-grounded dialogue systems tend to predict user-uninteresting and contextually irrelevant topics due to noise within side information sources.
Approach:	They propose a personalized topic selection model for topic-grounded dialogue that selectively aggregates side information to generate engaging responses.
Outcome:	The proposed model outperforms state-of-the-art models on multiple evaluation metrics.

Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory (2025.acl-long)

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Guangyue Peng, Tao Ge, Wen Luo, Wei Li, Houfeng Wang

LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability (2025.findings-emnlp)

Challenge:	Semiparametric language models (LMs) use static storage, which lacks learning capability and is disconnected from the internal information flow of the parametric models.
Approach:	They reconceptualize the non-parametric memory represented by kNN-LM as a learnable Mixture-of-Neighbors Induction Memory (MoNIM) this synergizes the induction capabilities of attention heads with the memorization strength of feed-forward networks .
Outcome:	The proposed model is a learnable Mixture-of-neighbors induction memory (MoNIM) it synergizes the induction capabilities of attention heads with the memorization strength of feed-forward networks (FFNs).

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Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei, Wen Ma, Yuxuan Zhou, Jian Wu, Bowen Yu, Zuozhu Liu, Junyang Lin

Challenge:	Existing benchmarks for long-form generation assess real-world queries with hard-to-verify metrics or use synthetic setups that overlook real-life intricacies.
Approach:	They propose a new approach that balances verifiable and real-world assessment with Target-Anchored Evaluation.
Outcome:	The proposed model balances real-world and verifiable assessment with Target-Anchored Evaluation (TAE) it generates queries, textual materials, and anchors based on verifier targets within real-life scenarios .

Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)

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Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei Zhang, Hai Zhu, Huajun Chen

Information Extraction & Text Mining

Challenge:	Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities.
Approach:	They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier.
Outcome:	The proposed method performs well in the current distant supervision dataset.

MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)

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Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng

Challenge:	Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data.
Approach:	They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data.
Outcome:	The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard.

Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)

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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen

Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue (2022.findings-emnlp)

Challenge:	Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks.
Approach:	They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs.
Outcome:	The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs.

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Xiao-Fei Wen, Wei Wei, Xian-Ling Mao

Dialogue Systems & Conversational Agents

Challenge:	Existing topic-grounded dialogue systems focus on the current conversation and corresponding topic sequence to predict the next topic.
Approach:	They propose a new approach to exploit topic-guided conversations to better model post-to-response topic-transition and guide the response generation to the current conversation.
Outcome:	The proposed model outperforms baselines on prediction and generation tasks.

SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models (2025.emnlp-main)

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Peng Ding, Wen Sun, Dailin Li, Wei Zou, Jiaming Wang, Jiajun Chen, Shujian Huang

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) excel at various tasks but are vulnerable to jailbreak attacks that induce harmful content generation.
Approach:	They propose a reinforcement learning framework that leverages the model’s own discrimination capabilities as a reward signal to enhance generation safety through iterative self-improvement.
Outcome:	The proposed framework improves model safety by iterative self-improvement without additional annotated data or external models during training phase.

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Yuyan Chen, Zhihao Wen, Ge Fan, Zhengyu Chen, Wei Wu, Dayiheng Liu, Zhixu Li, Bang Liu, Yanghua Xiao

RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)

Challenge:	Existing research emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs.
Approach:	They propose a model-adaptive prompt optimizer method that optimizes original prompts for each LLM in downstream tasks.
Outcome:	The proposed method can optimize prompts for an LLM in downstream tasks.

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Xuanwang Zhang, Yun-Ze Song, Yidong Wang, Shuyun Tang, Xinfeng Li, Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun Zhang, Qingsong Wen

Information Retrieval

Challenge:	Existing research on Retrieval Augmented Generation (RAG) does not address the problem of hallucinations and real-time updating of knowledge.
Approach:	They propose a modular open-source library to equip LLMs with external knowledge.
Outcome:	The proposed approach reduces the need for expensive open-source tools and lacks fair comparisons between novel RAG algorithms.

Robustness Testing of Language Understanding in Task-Oriented Dialog (2021.acl-long)

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Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang

Robustness in NLP

Challenge:	a lack of systematic studies on the robustness of language understanding models in task-oriented dialog systems is limiting . authors propose a model-agnostic toolkit LAUG to approximate natural language perturbations .
Approach:	They propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness of language understanding models in task-oriented dialog systems.
Outcome:	The proposed toolkit reveals critical robustness issues in state-of-the-art models.

Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding (2025.findings-acl)

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Feifan Song, Shaohang Wei, Wen Luo, Yuxuan Fan, Tianyu Liu, Guoyin Wang, Houfeng Wang

E-ViC: Reasoning Beyond Text via Embodied Visual Chain for Spatial Intelligence (2026.acl-long)

Challenge:	Low-resource methods for LLM alignment have been popular, but still face challenges in obtaining high-quality and aligned content.
Approach:	They propose a framework to enhance alignment ability of base models by the guidance of a small aligned model.
Outcome:	The proposed framework outperforms baseline methods while avoiding degradation on downstream tasks.

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Junbo Qi, Yi Zhang, Hanchu Ni, Che Liu, Zhimin Yao, Ruilin Yang, Xiancong Ren, Liangjian Wen, Wei Ge, Yuya Ieiri, Osamu Yoshie, Yong Dai, Xiaozhu Ju

Multimodality

Challenge:	Existing Vision-Language Models (VLMs) lack spatial reasoning, despite text-based CoTs . e-ViC reframes spatial intelligence as a verifiable, tool-using capability, argues a new study.
Approach:	They propose a framework that moves reasoning beyond text into the visual domain . they ground reasoning in pixel-level interactions to enable human-like "look-and-confirm" strategies .
Outcome:	The proposed framework outperforms existing Vision-Language Models with an average gain of 10.1%.

Bridging the Sensory Gap: Visual Injection for Taxonomy Completion (2026.acl-long)

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Yuhang Niu, Hongyuan Xu, Ciyi Liu, Bofan Wei, Jiaqi Ye, Yanlong Wen, Xiaojie Yuan

Multimodality

Challenge:	Existing text-only methods suffer from a "Sensory Gap" in integrating new concepts into existing hierarchies.
Approach:	They propose a framework leveraging Visual Injection for Taxonomy Completion that maps synthesized images into intrinsic pseudo-tokens and decouples magnitude from selection to prevent visual signals from being drowned out.
Outcome:	Experiments on three datasets show that VITC achieves state-of-the-art performance . it delivers an average absolute gain of over 19% in Hit@1.

Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models (2024.acl-long)

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Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, Ji-Rong Wen

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs (D19-1)

Challenge:	Despite the impressive multilingual capabilities demonstrated by LLMs, the understanding of how these abilities develop and function remains nascent.
Approach:	They propose a novel detection method to pinpoint language-specific neurons within LLMs by selectively activating or deactivating these neurons.
Outcome:	The proposed method can “steer” the output language of LLMs by selectively activating or deactivating language-specific neurons.

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Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, Huajun Chen

GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence (2023.findings-emnlp)

Challenge:	Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.
Approach:	They propose a Meta Relational Learning framework to do few-shot link prediction in KGs by observing only a few associative triples.
Outcome:	The proposed model achieves state-of-the-art results on few-shot link prediction KG benchmarks.

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Zhihua Wen, Zhiliang Tian, Wei Wu, Yuxin Yang, Yanqi Shi, Zhen Huang, Dongsheng Li

Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation (2021.acl-long)

Challenge:	Existing methods for generating stories with complex plots rely on detailed prompts, which inadvertently limit the creative potential of the generated stories.
Approach:	They propose a retrieval-auGmented stoRy generation framework with a fOrest of eVidEnce to enhance stories’ complexity.
Outcome:	The proposed framework enables generating more diverse plotlines from human-written stories.

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Tong Zhang, Long Zhang, Wei Ye, Bo Li, Jinan Sun, Xiaoyu Zhu, Wen Zhao, Shikun Zhang

Multilinguality

Challenge:	Existing approaches to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models have been criticized for lack of integration of bilingual lexical information into the neural architecture.
Approach:	They propose a neural architecture to incorporate bilingual dictionaries into Neural Machine Translation models by introducing three new components: Pointer, Disambiguator, and Copier.
Outcome:	The proposed method achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionary can potentially be used; (2) Disambiguator synthesizes contextual information from source view and target view, both of which contribute to distinguishing translation of a specific source word from multiple candidates in dicaries; (3) Copier systematically connects Pointer and Disambiguators based on a hierarchical

Building an Ellipsis-aware Chinese Dependency Treebank for Web Text (L18-1)

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Xuancheng Ren, Xu Sun, Ji Wen, Bingzhen Wei, Weidong Zhan, Zhiyuan Zhang

Syntactic Text Processing

Challenge:	ellipsis is a common linguistic phenomenon that some words are left out as they are understood from the context, especially in oral utterance.
Approach:	They propose to use a Chinese dependency treebank to facilitate the parsing of web text . they propose to restore omissions and reserve contexts in the web text to improve dependency parsers .
Outcome:	The proposed framework enables the parsing of web text from online microblogs.

Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models (2021.findings-acl)

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Junyi Li, Tianyi Tang, Wayne Xin Zhao, Zhicheng Wei, Nicholas Jing Yuan, Ji-Rong Wen