Papers by Ping Li

OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework (2022.acl-long)

Challenge:	Existing pruning methods require inefficient retraining for billion-scale LLMs or rely on heuristicically designed metrics to determine pruning masks, leading to performance degradation.
Approach:	They propose a convex optimization model that induces sparsity in large language models by leveraging FISTA.
Outcome:	The proposed method can remove 50% of model parameters while retaining 98.6% and 95.6% of the zero-shot performance.

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Xin Wang, Minlong Peng, Mingming Sun, Ping Li

Denoising Enhanced Distantly Supervised Ultrafine Entity Typing (2023.findings-acl)

Challenge:	Different Open Information Extraction (OIE) tasks require different types of information.
Approach:	They propose to adapt an OIE Graph to different OIE tasks with simple rules . they implement an end-to-end OIA generator and make it open-accessible .
Outcome:	The proposed system achieves new SOTA performance on three popular OIE tasks.

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Yue Zhang, Hongliang Fei, Ping Li

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

Challenge:	Recent work on distantly supervised (DS) ultra-fine entity typing has received significant attention . however, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall.
Approach:	They propose a noise model to estimate unknown labeling noise distribution over input contexts and noisy type labels and a model to train on denoised data.
Outcome:	The proposed model outperforms baseline methods on the Ultra-Fine entity typing dataset and OntoNotes dataset.

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

Green & Sustainable NLP

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval (2022.findings-naacl)

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Jiaheng Liu, Tan Yu, Hanyu Peng, Mingming Sun, Ping Li

Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning (2022.naacl-main)

Challenge:	Existing multilingual video corpus moment retrieval methods are based on a two-stream structure.
Approach:	They propose a multilingual video corpus moment retrieval task that uses a two-stream structure to generate a query-visual similarity and a subtitle stream exploits the query-subtitle similarity.
Outcome:	The proposed method improves accuracy on a large-scale video corpus moment retrieval dataset.

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Haiyan Yin, Dingcheng Li, Ping Li

Text Generation

Challenge:	Paraphrase generation is an important natural language generation task . however, the effectiveness of paraphrase generation can be limited due to the limited data available.
Approach:	They propose a weakly supervised approach to paraphrase generation that leverages reinforcement learning for effective model training with data selection.
Outcome:	The proposed model improves the state-of-the-art performance on four weakly supervised paraphrase generation tasks.

On Efficient Retrieval of Top Similarity Vectors (D19-1)

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Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li

Green & Sustainable NLP

Challenge:	Existing representation learning methods such as Word2vec represent word embeddings in the semantic space.
Approach:	They propose an efficient method for searching vectors via a non-metric matching function: inner product.
Outcome:	Experiments on data representations learned for different machine learning tasks show the proposed method outperforms existing methods.

Hypergraph based Understanding for Document Semantic Entity Recognition (2024.acl-long)

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Qiwei Li, Zuchao Li, Ping Wang, Haojun Ai, Hai Zhao

Temporal Token Matters: Investigating and Interpreting the Consistency of Temporal Ordering in Large Language Models (2026.findings-acl)

Challenge:	Existing document understanding models focus on entity categories while ignoring the extraction of entity boundaries.
Approach:	They propose a hypergraph attention document semantic entity recognition framework which uses hypergraph focus to focus on entity boundaries and entity categories at the same time.
Outcome:	The proposed framework can improve the performance of existing models on FUNSD, CORD, XFUND and SROIE.

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Zhen Yang, Xinyue Zhang, Ping Jian, Chengzhi Li, Zhongbin Guo, Jiaping Feng, Wenpeng Lu

Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) exhibit notable deficiencies in temporal reasoning . phrasing changes can lead LLMs to produce inconsistent outputs .
Approach:	They investigate the mechanistic interpretability of temporal ordering within event temporal reasoning . they identify a sparse subset of attention heads that are causally responsible for reasoning outcomes .
Outcome:	The proposed model outperforms other models in a variety of tasks and is validated by intervention-based experiments.

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Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar

Rethinking Sentiment Style Transfer (2021.findings-emnlp)

Challenge:	Existing methods for image-to-text generation store all knowledge within parameters, thus requiring computational-expensive fine-tuning.
Approach:	They propose a Retrieval-augmented Visual Language Model that stores all the knowledge within parameters and can be used to retrieve it from the external database.
Outcome:	The proposed model significantly boosts performance for image-to-text generation tasks with 4x less parameters compared with baseline methods.

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Ping Yu, Yang Zhao, Chunyuan Li, Changyou Chen

Sentiment Analysis

Challenge:	Existing evaluation methods for text style transfer are unsatisfactory.
Approach:	They propose to use a graph-based method to extract attribute content from sentences . they propose an efficient regularization to leverage attribute-dependent content as guiding signals.
Outcome:	The proposed method is based on a YELP and IMDB dataset and it is able to detect errors in the human evaluation.

RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding (2026.findings-acl)

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Zihong Zhang, Zuchao Li, Lefei Zhang, Ping Wang, Hai Zhao

TRACE: Traversal Retrieval-Augmented Chain of Evidence for Document Understanding (2026.acl-long)

Challenge:	Existing methods for decoding large language models generate one token per step, causing high inference latency.
Approach:	They propose a method that integrates retrieved exact patterns with logit-driven future cues.
Outcome:	Experiments on Spec-Bench, HumanEval, and MGSM-ZH show that RACER outperforms training-free methods and accelerates inference.

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Liqi He, Zuchao Li, Hao Huang, Ping Wang

Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification (2024.emnlp-main)

Challenge:	Long-context Document Visual Question Answering (DocVQA) methods struggle with visual semantics or handling finite context windows.
Approach:	They propose a new approach to longcontext document visual question answering that transforms retrieval into adaptive evidence chain construction using a Bi-Layered Graph.
Outcome:	The proposed approach achieves an average accuracy improvement of 14.07% on M5BookVQA and exhibits robust generalization with a 13.38% gain across four established benchmarks.

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Pinyi Zhang, Jingyang Chen, Junchen Shen, Zijie Zhai, Ping Li, Jie Zhang, Kai Zhang

Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)

Challenge:	Emotion classification is an important task with applications in education, virtual reality, and robotics.
Approach:	They propose to use token embeddings to generate a "semantic-anchor graph" using semantic anchors, sentences can be projected onto them to form a graph .
Outcome:	Empirically, the proposed system can generate meaningful semantic anchors and discriminative graph patterns for different emotion.

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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang

Defending against Insertion-based Textual Backdoor Attacks via Attribution (2023.findings-acl)

Challenge:	Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability.
Approach:	They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs .
Outcome:	The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks.

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Jiazhao Li, Zhuofeng Wu, Wei Ping, Chaowei Xiao, V.G.Vinod Vydiswaran

Robustness in NLP

Challenge:	Textual backdoor attacks are vulnerable to backdoors and can be used to infect models trained on poisoned data.
Approach:	They propose an efficient attribution-based pipeline to defend against two insertion-based poisoning attacks, BadNL and InSent.
Outcome:	The proposed method can generalize sufficiently well in two common attack scenarios, which consistently improves previous methods.

Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews (D19-1)

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Miao Fan, Chao Feng, Mingming Sun, Ping Li

Representation Learning

Challenge:	a helpful review is largely concerned with the metadata of its target product . a selector learns from both the key-value product metadata and one of its reviews to take an action .
Approach:	They propose a framework that uses product metadata to assess helpfulness of free-text reviews . they use two real-world datasets from amazon.com and Yelp.com to test the framework .
Outcome:	The proposed framework can achieve state-of-the-art performance with substantial improvements . it uses two real-world datasets from Amazon.com and Yelp.com .

Can Large Language Models Be Good Language Teachers? (2025.emnlp-main)

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LiQing Xu, Qiwei Li, Tianshuo Peng, Zuchao Li, Hai Zhao, Ping Wang

Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering (2026.acl-long)

Challenge:	Large language models (LLMs) have achieved remarkable success across diverse domains, but their potential as effective language teachers remains inadequately assessed.
Approach:	They propose a framework to evaluate Chinese language teachers' pedagogical competence against international standards.
Outcome:	The proposed framework evaluates 13 latest multilingual and Chinese LLMs against international standards for Chinese language teachers.

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Zizhen Wang, Bo Feng, Zhengfeng Lai, Shiyu Li, Yang Lu, Meng Cao, Ping Huang, Xiaoming Simon Wang

Teaching Large Language Models to Translate on Low-resource Languages with Textbook Prompting (2024.lrec-main)

Challenge:	Existing metrics for video captioning are based on text-based comparisons with ground-truth references.
Approach:	They propose a reference-free benchmark that assesses video captions based on their utility . they will release the benchmark to facilitate reproducible research .
Outcome:	The proposed benchmark improves on human-verified, fine-grained questions . it correlates significantly better with human judgments than existing metrics .

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Ping Guo, Yubing Ren, Yue Hu, Yunpeng Li, Jiarui Zhang, Xingsheng Zhang, Heyan Huang

Machine Translation

Challenge:	Large Language Models (LLMs) have demonstrated impressive results in Machine Translation by following instructions, even without training on parallel data.
Approach:	They propose a Translate After LEarNing Textbook approach which aims to enhance LLMs’ ability to translate low-resource languages by learning from a textbook.
Outcome:	The proposed approach improves translation performance by 14.8% using 112 low-resource languages from FLORES-200 with two LLMs: ChatGPT and BLOOMZ.

Cognitive Scaffold: From Fluid Context to Crystallized Memory for Long-Horizon DeepResearch Agents (2026.acl-long)

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Qiuyuan Ai, Zenghuang Fu, Zhaoyang Li, Ping Jiang, Haoyu Wu, Jie Song, Guannan He

Reasoning

Challenge:	Scaling LLM-based agents to long-horizon deep research is constrained by context-noise trade-off . solving a single query may require hundreds of interactions with noisy environments .
Approach:	They propose a factorized memory architecture that decouples the cognitive state into a Fluid Working Context for immediate reasoning and a persistent Knowledge Graph for long-term retention.
Outcome:	The Cognitive Scaffold outperforms baselines on Xbench-DeepSearch, BrowseComp-ZH, and GAIA . it achieves 74.7% Avg@3 and 87.0% Pass@3 on xbench, browseComp, and 88.3% Pass@3.

MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues (2026.findings-acl)

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Yaning Pan, Qianqian Xie, Guohui Zhang, Zekun Moore Wang, Yongqian Wen, Yuanxing Zhang, Haoxuan Hu, Zhiyu Pan, Yibing Huang, Zhidong Gan, Yonghong Lin, An Ping, Shihao Li, Yanghai Wang, Tianhao Peng, Jiaheng Liu

Natural Language Interfaces

Challenge:	Existing evaluation benchmarks for Multimodal Large Language Models (MLLMs) focus on single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios.
Approach:	They propose a video understanding benchmark for MLLMs in multi-turn dialogues that assesses six core competencies that focus on perceptivity and interactivity.
Outcome:	The MT-Video-Bench evaluates 1,000 multi-turn dialogues from diverse domains and reveals significant performance discrepancies and limitations in handling multi-turned video dialogues.

A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation (2021.findings-emnlp)

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Dingcheng Li, Hongliang Fei, Shaogang Ren, Ping Li

Representation Learning

Challenge:	Recent advances in disentanglement work on coarse levels in the disenanglement of closely related properties, such as syntax and semantics in human languages.
Approach:	They propose a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences.
Outcome:	The proposed model significantly improves the disentanglement quality between syntactic and semantic representations for semantic similarity tasks and syntaktic similarity task.

Faster In-Context Learning for LLMs via N-Gram Trie Speculative Decoding (2025.emnlp-main)

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Jinglin Chen, Qiwei Li, Zuchao Li, Baoyuan Qi, Liu Guoming, Haojun Ai, Hai Zhao, Ping Wang

ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)

Challenge:	In-Context Learning (ICL) is a key method in prompt engineering, but its long retrieved contexts and limited token throughput will slow reasoning speeds.
Approach:	They propose a method that leverages the overlap between context and model output to generate drafts from the context.
Outcome:	The proposed method achieves the highest mean speedup on Vicuna-7B, Llama2-7B-Chat, and Llma3-8B-Instruct tasks.

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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang

Structured Data in NLP

Challenge:	retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures .
Approach:	They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents.
Outcome:	The proposed method reduces offline indexing costs and accelerates retrieval.

Inflate and Shrink:Enriching and Reducing Interactions for Fast Text-Image Retrieval (2021.emnlp-main)

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Haoliang Liu, Tan Yu, Ping Li

Accurate KV Cache Quantization with Outlier Tokens Tracing (2025.acl-long)

Challenge:	a recent study shows that late-interaction methods trade off retrieval accuracy and efficiency by exploiting cross-modal interactions only in the late stage.
Approach:	They propose an inflating and shrinking approach to exploit cross-modal interactions . they inflate code inputs and shrink code outputs to exploit interactions progressively .
Outcome:	The proposed method exploits cross-modal interactions in the late stage to achieve retrieval speed.

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Yi Su, Yuechi Zhou, Quantong Qiu, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang

Vista-LLM: Decoupled Query-Guided Visual Token Pruning for Efficient Long-Video Large Language Models (2026.acl-long)

Challenge:	Large Language Models (LLMs) require substantial computational resources during deployment.
Approach:	They propose a method to identify outlier tokens and exclude them from quantization . they find that the method can deliver a 6.4 times reduction in memory usage and a 2.5 times increase in throughput .
Outcome:	The proposed method delivers a 6.4 times reduction in memory usage and a 2.5 times increase in throughput under 2-bit quantization.

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Zhenyu Li, Zuchao Li, Ping Wang, Lefei Zhang, Haojun Ai

Joint Learning from Labeled and Unlabeled Data for Information Retrieval (C18-1)

Challenge:	Long-video understanding is bottlenecked by the high cost of processing massive visual tokens.
Approach:	They propose a decoupled framework for query-guided visual token pruning . their method reduces visual tokens by 90% and accelerates inference by 98% .
Outcome:	The proposed framework reduces visual tokens by 90% and accelerates inference while retaining over 98% of baseline performance on average.

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Bo Li, Ping Cheng, Le Jia

Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching (2026.acl-long)

Challenge:	Recent studies have focused on neural information retrieval (IR) models.
Approach:	They propose a framework which can benefit from both labeled and more abundant unlabeled data . they propose supervised retrieval over several strong baselines for IR .
Outcome:	The proposed framework can benefit from labeled and more abundant unlabeled data for representation learning in the context of IR.

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Bo Lv, Jingbo Sun, Jianwei Lv, Chen Tang, Shaojie Zhang, Nayu Liu, Guoxin Yu, Zihao Li, Qichao Zhang, Dongbin Zhao, Ping Luo, Yue Yu

Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge (2022.findings-naacl)

Challenge:	Existing routing methods rely on direct mapping from queries to models based on surface-level features, leading to poor generalizability on out-of-distribution data.
Approach:	They propose a new routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs.
Outcome:	The proposed framework improves matching accuracy while lowering inference costs . it decouples linguistic surface forms from task-intrinsic requirements .

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Yue Feng, Zhen Han, Mingming Sun, Ping Li

Structured Data in NLP

Challenge:	Existing open-domain question answering systems only select one source to generate answer or conduct reasoning on structured information.
Approach:	They propose a Document-Entity Heterogeneous Graph Network to integrate different sources of information and conduct reasoning on heterogeneous information.
Outcome:	The proposed model outperforms the state-of-the-art methods on a HybirdQA dataset.

From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs (2025.findings-acl)

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Guocong Li, Weize Liu, Yihang Wu, Ping Wang, Shuaihan Huang, Hongxia Xu, Jian Wu

Reasoning

Challenge:	Existing methods focus on correcting the output but overlook the ability of LLMs to detect and correct misleading content in the input itself.
Approach:	They propose a three-stage fine-tuning method that improves LLMs' ability to detect and correct misleading information in input queries.
Outcome:	The proposed method improves accuracy and factuality of LLM responses while also reducing hallucinations.

Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain (D18-1)

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Mingming Sun, Xu Li, Ping Li

Open Information Extraction

Challenge:	Experimental results reveal dual structure between OIE and OIN tasks helps to build better OIE agents and OINE agents.
Approach:	They propose an Open-Domain Information Narration task as the reverse task of Open Information Extraction (OIE) they then propose an OIN task as an OIE agent and an OIR agent to implement the dual structure .
Outcome:	The proposed task is the reverse task of Open Information Extraction (OIE) The proposed system is able to implement the dual structure with a reinforcement learning paradigm.

Option Symbol Matters: Investigating and Mitigating Multiple-Choice Option Symbol Bias of Large Language Models (2025.naacl-long)

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Zhen Yang, Ping Jian, Chengzhi Li

ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access (2026.eacl-demo)

Challenge:	Multiple-Choice Question Answering (MCQA) is a widely used task in the evaluation of large language models (LLMs).
Approach:	They propose a tuning-free, causal effect driven debiasing method which intervenes the activations of identified components according to their causal effects.
Outcome:	The proposed method alleviates the aforementioned bias and improves the performance of LLMs.

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Jiwoo Park, Ruoqi Liu, Avani Jagdale, Andrew Srisuwananukorn, Jing Zhao, Lang Li, Ping Zhang, Sachin Kumar

Question Answering

Challenge:	ClinicalTrialsHub consolidates clinical trial data from ClinicalTrial.gov and augments it by extracting and structuring trial-relevant information from PubMed.
Approach:	They propose a search-focused platform that consolidates PubMed data and extracts structured trial information.
Outcome:	ClinicalTrialsHub increases access to structured clinical trial data by 83.8% compared to ClinicalTrial.gov alone.

Dual-Channel Span for Aspect Sentiment Triplet Extraction (2023.emnlp-main)

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Pan Li, Ping Li, Kai Zhang

Sentiment Analysis

Challenge:	Existing approaches to extract sentiment triplets are too noisy and enumerate all possible spans.
Approach:	They propose a dual-channel span generation method to constrain the search space of span candidates.
Outcome:	The proposed method reduces span enumeration by nearly half on two versions of public datasets.

Induction Networks for Few-Shot Text Classification (D19-1)

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Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu, Ping Jian, Jian Sun

Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process (N19-1)

Challenge:	Recent studies have used meta-learning to simulate the few-shot task . however, this sample-wise comparison may be severely disturbed by the various expressions in the same class.
Approach:	They propose a meta-learning-based induction network to learn a generalized class-wise representation of each class in a support set.
Outcome:	The proposed model outperforms existing state-of-the-art models on a sentiment and dialogue intent datasets.

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Dingcheng Li, Siamak Zamani, Jingyuan Zhang, Ping Li

V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models (2026.findings-acl)

Challenge:	Topic models are used to extract topical structures from document-word frequency representations of the text corpus without supervision.
Approach:	They propose a Bayesian nonparametric topic modeling with knowledge graph embedding to employ knowledge graphs to extract more coherent topics.
Outcome:	The proposed model performs better on three public datasets than state-of-the-art models on topic coherence and document classification accuracy.

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Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang, Rui Yan, Yuan Yao, Lijuan Wang

Beyond Templates: Dynamic Adaptation of Reasoning Demonstrations via Feasibility-Aware Exploration (2026.findings-acl)

Challenge:	Existing static image-text benchmarks are insufficient for evaluating multimodal large language models’ dynamic perception and interactive reasoning abilities.
Approach:	They propose a game-based evaluation framework to assess multimodal large language models’ visual reasoning in dynamic, continuous-space environments.
Outcome:	The proposed framework systematically assesses MLLMs’ visual reasoning in dynamic, continuous-space environments.

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Yong Wu, Weihang Pan, Ke Li, Chen Binhui, Ping Li, Binbin Lin

Punctuation-Steered Representation Fine-Tuning (2026.acl-short)

Challenge:	Existing reasoning datasets that are designed for powerful LLMs often lead to degraded performance when directly applied to weaker models.
Approach:	They propose a data adaptation framework that bridges the capability gap between expert reasoning trajectories and diverse SLMs by employing a selective imitation strategy guided by step-wise adaptability estimation via solution simulation.
Outcome:	The proposed framework improves generalization and data efficiency over static fine-tuning and can be applied to large models with limited model capacity.

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Zheng Gong, Ying Sun, Ping Li, Yi Zheng, Zhefeng Wang

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)

Challenge:	Existing methods for parameter-efficient fine-tuning (PeFT) are limited due to their prohibitive size and computational demands.
Approach:	They propose a method that fine-tunes punctuation representations to achieve performance improvements.
Outcome:	The proposed method improves performance by altering the representation space alone . but it results in suboptimal performance due to the effects of the method on the output .

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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen

Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model (2022.coling-1)

Challenge:	Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge.
Approach:	They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance.
Outcome:	Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points.

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Sixing Wu, Ying Li, Ping Xue, Dawei Zhang, Zhonghai Wu

Dialogue Systems & Conversational Agents

Challenge:	Pre-trained language models can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding.
Approach:	They propose a two-stage framework to integrate a linearized knowledge into plan text using a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact.
Outcome:	The proposed framework improves the performance of pre-trained language models by using section-aware strategies to encode the linearized knowledge.

Constructing Your Model’s Value Distinction: Towards LLM Alignment with Anchor Words Tuning (2025.findings-emnlp)

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Zhen Yang, Ping Jian, Chengzhi Li, Chenxu Wang, Xinyue Zhang, Wenpeng Lu

Challenge:	a study of large language models (LLMs) shows that they can generate outputs that are honest, positive, harmless, etc.
Approach:	They propose a method that amplifies logits difference between positive and negative tokens . they propose to use the logits gap to generate positive and positive tokens after alignment .
Outcome:	The proposed method achieves effective alignment, but requires fewer computational resources compared to training-time alignment methods.

End-to-end Deep Reinforcement Learning Based Coreference Resolution (P19-1)

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Hongliang Fei, Xu Li, Dingcheng Li, Ping Li

Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension (2026.findings-acl)

Challenge:	Recent neural network models for coreference resolution are usually trained with heuristic loss functions that are computed over a sequence of local decisions.
Approach:	They propose an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics.
Outcome:	The proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.

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Hongbo Zhao, Huibin Wang, Bin Tang, Xianming Hu, Yihong Huang, Yijun Shen, Nuoyi Chen, Ping Li, Kai Zhang

NOTA: Multimodal Music Notation Understanding for Visual Large Language Model (2025.findings-naacl)

Challenge:	Existing training-free methods for extrapolating beyond training context lengths are semantics-agnostic . Existing methods that focus on relative token distances can indiscriminately blur semantically relevant and irrelevant tokens .
Approach:	They propose an adaptive positional zooming method that uses semantic relevance to extrapolate beyond training context lengths.
Outcome:	Experiments show that RiPRA outperforms existing training-free extrapolation methods . relevant tokens get higher positional resolution, while irrelevant tokens are compressed .

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Mingni Tang, Jiajia Li, Lu Yang, Zhiqiang Zhang, Jinhao Tian, Zuchao Li, Lefei Zhang, Ping Wang

Faster MoE LLM Inference for Extremely Large Models (2026.findings-acl)

Challenge:	Existing general-domain visual language models lack ability of music notation understanding . Symbolic music is represented in two distinct forms: auditory music and symbolic music .
Approach:	They propose to train a multimodal music notation model using a large-scale dataset . they use cross-modal alignment to train the model for music notations analysis .
Outcome:	The proposed model improves on music understanding by training with a multimodal music notation model.

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Haoqi Yang, Luohe Shi, Qiwei Li, Zuchao Li, Ping Wang, Hao Huang, Hai Zhao

PromptGen: Automatically Generate Prompts using Generative Models (2022.findings-naacl)

Challenge:	Existing inference optimizations for coarse-grained Mixture-of-Experts models implicitly assume a fixed activation budget, which is poorly understood.
Approach:	They propose a training-free policy that adapts token-level activation using router confidence and entropy while remaining within the model’s original budget.
Outcome:	The proposed skipping policy can provide substantial throughput gains, but optimal static schedules vary significantly across models and routing mechanisms.

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Yue Zhang, Hongliang Fei, Dingcheng Li, Ping Li

BoYaEval: Evaluating Multimodal Large Language Models on Understanding Ancient Chinese Musical Scores (2026.acl-long)

Challenge:	Recent prompt learning has received significant attention, where downstream tasks are reformulated to the mask-filling task with the help of a textual prompt.
Approach:	They propose a model PromptGen which can automatically generate prompts conditional on the input sentence.
Outcome:	The proposed model outperforms baseline models on the knowledge probing LAMA benchmark.

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Jiajia Li, Weizhi Xue, Yao Yao, Qiwei Li, null Chenchong, Zuchao Li, Ping Wang, Hai Zhao

DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)

Challenge:	Multimodal Large Language Models excel in general tasks but struggle with specialized, structured cultural symbols.
Approach:	They evaluate 21 leading MLLMs and compare their performance to a benchmark for Ancient Chinese musical notation.
Outcome:	The benchmark evaluates 21 leading MLLMs on five types of ancient Chinese music notation systems.

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Yubing Ren, Yanan Cao, Hao Li, Yingjie Li, Zixuan ZM Ma, Fang Fang, Ping Guo, Wei Ma

Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)

Challenge:	Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model .
Approach:	They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments.
Outcome:	The proposed dataset has over 56,000+ events and 242,000+ arguments.

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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen

EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (2020.acl-demos)

Challenge:	Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges.
Approach:	They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task.
Outcome:	The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing.

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Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, Jiawei Han

VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search (2025.emnlp-main)

Challenge:	EVIDENCEMINER is a web-based system that allows users to query a natural language statement and retrieve textual evidence from a background corpora for life sciences.
Approach:	They propose a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences.
Outcome:	EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences.

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Yiming Jia, Jiachen Li, Xiang Yue, Bo Li, Ping Nie, Kai Zou, Wenhu Chen

How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study (2026.acl-long)

Challenge:	Existing vision-language models struggle with reasoning-focused tasks due to the lack of high-quality training data.
Approach:	They propose a new approach that leverages search engines to create a multimodal multimodal dataset . they use a set of 30,000 seed images to extract HTML data from 700K unique URLs .
Outcome:	The proposed model achieves the best known performance on MMMU-Pro (40.7), MathVerse (42.6), and DynaMath (55.7).

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Zhen Yang, Ping Jian, Zhongbin Guo, Zuming Zhang, Chengzhi Li, Yonghong Deng, Xinyue Zhang, Wenpeng Lu

Explainability & Interpretability in NLP

Challenge:	Existing studies on spatial intelligence from the perspective of visual-spatial intelligence have not explored whether visual intelligence alone is sufficient to endow models with spatial intelligence.
Approach:	They propose to use a linguistic perspective to investigate spatial intelligence from a theoretical perspective.
Outcome:	The proposed model performs poorly on the proposed dataset while human can easily achieve 100% accuracy.

Text2World: Benchmarking Large Language Models for Symbolic World Model Generation (2025.findings-acl)

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Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, Ping Luo

SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers (2025.acl-long)

Challenge:	Recent studies have encountered limitations in leveraging large language models to generate symbolic world models.
Approach:	They propose a benchmarking framework based on planning domain definition language (PDDL) that employs multi-criteria, execution-based metrics for a more robust evaluation.
Outcome:	The proposed model outperforms models trained with large-scale reinforcement learning, but lacks the robustness needed to perform in world modeling.

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Zicong Tang, Shi Luohe, Zuchao Li, Baoyuan Qi, Liu Guoming, Lefei Zhang, Ping Wang

Challenge:	Large language models (LLMs) have impressive capabilities across various fields, but their widespread use is facing a severe and realistic challenge, which is their high demand for GPU memory.
Approach:	They propose a KV cache reduction method which balances both shallow and deep layers by using an attention weight based eviction method and a codebook based replacement approach.
Outcome:	The proposed method reduces the KV cache for shallower layers while preserving similar or even better model performance.

Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition (2021.naacl-main)

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Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie Zhou

The Music Maestro or The Musically Challenged, A Massive Music Evaluation Benchmark for Large Language Models (2024.findings-acl)

Challenge:	Existing models fail to fully utilize contextual information which plays an important role in interpreting sentences.
Approach:	They propose a graph-based Context Tracking Network to model the discourse context for IDRR.
Outcome:	The proposed model can integrate sentence-level and token-level contextual semantics better than existing models.

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Jiajia Li, Lu Yang, Mingni Tang, Chenchong Chenchong, Zuchao Li, Ping Wang, Hai Zhao

Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)

Challenge:	Existing benchmarks to evaluate LLMs' capabilities are inadequate for assessing their musical capabilities.
Approach:	They propose to use a large-scale music benchmark specifically designed to evaluate the music-related capabilities of large language models (LLMs).
Outcome:	The proposed framework evaluates 16 large language models in the domain of music.

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Hongliang Fei, Tan Yu, Ping Li

Text Generation

Challenge:	Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English.
Approach:	They propose a new approach to learn cross-lingual cross-modal representations for matching images and captions in multiple languages using an annotated corpus.
Outcome:	The proposed model achieves impressive performance on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions.

BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)

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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin

SpARK: An Embarrassingly Simple Sparse Watermarking in LLMs with Enhanced Text Quality (2026.findings-eacl)

Challenge:	Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors .
Approach:	They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents.
Outcome:	The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries.

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Duy Cao Hoang, Thanh Quoc Hung Le, Rui Chu, Ping Li, Weijie Zhao, Yingjie Lao, Khoa D Doan

Challenge:	Existing methods for detecting and monitoring generated text face a trade-off between the quality of the generated text and the effectiveness of the watermarking process.
Approach:	They propose a new type of LLM watermark, Sparse WatermARK, which uses watermarks to a small subset of generated tokens distributed across the text.
Outcome:	The proposed method outperforms existing methods in detectability and quality while maintaining generated text quality.

From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models (2026.findings-acl)

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Chejian Xu, Wei Ping, Peng Xu, Zihan Liu, Boxin Wang, Mohammad Shoeybi, Bo Li, Bryan Catanzaro

Debate-of-Thoughts: Resolving Knowledge Conflicts in LLMs Through Internal Deliberation (2026.acl-long)

Challenge:	Long-context capabilities are essential for document and video understanding, in-contact learning, and inference-time scaling.
Approach:	They propose an efficient training recipe for building ultra-long context LLMs from aligned instruct model, pushing the boundaries of context lengths from 128K to 1M, 2M, and 4M tokens.
Outcome:	The proposed model extends the context window while maintaining short context capabilities while maintaining the performance of the existing model.

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Guocong Li, Qirui Hu, Ping Wang, Guofeng Zhang, Jian Wu, Hongxia Xu

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)

Challenge:	Existing methods for retrieval augmented generation are based on a simplistic binary choice of relying on external contexts or memory.
Approach:	They propose a framework that transforms conflict resolution into an active deliberation process by incorporating contradictions as opportunities for deeper reasoning.
Outcome:	Experiments show that DoT outperforms state-of-the-art methods while generating transparent debate transcripts that explain its decisions.

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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro

ATQ: Activation Transformation forWeight-Activation Quantization of Large Language Models (2024.findings-emnlp)

Challenge:	a recent study shows that retrieval-augmented LMs can improve text generation quality and accuracy.
Approach:	They propose a model that reproduces RETRO parameters while retrieving a text corpus . they find RETRO outperforms GPT on text generation with less repetition .
Outcome:	The proposed model outperforms standard retrieval-augmented GPT and retrieval augmented GTP on text generation and accuracy tasks.

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Yundong Gai, Ping Li

Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)

Challenge:	Quantization methods are available to solve the problem of high computational and storage costs for Large language models.
Approach:	They propose an INT8 weight-activation quantization method that can achieve lossless accuracy.
Outcome:	The proposed method can achieve lossless accuracy on OPT and LLaMA families.

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Rui Yin, Tianxu Han, Naen Xu, Changjiang Li, Ping He, Chunyi Zhou, Jun Wang, Zhihui Fu, Tianyu Du, Jinbao Li, Shouling Ji

Selective Prefix Tuning for Pre-trained Language Models (2024.findings-acl)

Challenge:	Existing methods to inject safety-aligned large language models rely on token-level mappings, which do not guarantee sustained harmful output.
Approach:	They propose a method that directly modifies model weights to map a trigger to an attacker-specified response.
Outcome:	The proposed method achieves high triggered attack success while maintaining non-triggered safety and general utility.

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Hongyi Zhang, Zuchao Li, Ping Wang, Hai Zhao

Intra-Correlation Encoding for Chinese Sentence Intention Matching (2020.coling-main)

Challenge:	Existing methods for fine-tuning pre-trained models are time-consuming and memory-inefficient.
Approach:	They propose a method that inserts learnable vectors into each Transformer layer . they propose SL to encourage diversity in prefix tokens .
Outcome:	Extensive experiments validate the effectiveness of Prefix Tuning in sentence and token classification tasks.

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Xu Zhang, Yifeng Li, Wenpeng Lu, Ping Jian, Guoqiang Zhang

VHASR: A Multimodal Speech Recognition System With Vision Hotwords (2024.emnlp-main)

Challenge:	Existing methods to improve sentence intention matching for Chinese text are limited due to the particularity of the text.
Approach:	They propose a method that combines character-granularity and word-granulularity features to perform sentence intention matching.
Outcome:	The proposed method can capture sentence feature information from multiple perspectives and correlation information between different levels of sentences.

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Jiliang Hu, Zuchao Li, Ping Wang, Haojun Ai, Lefei Zhang, Hai Zhao

Experience-Driven Multi-Agent Optimization for Black-Box Jailbreak Attacks on Large Language Models (2026.findings-acl)

Challenge:	Existing models that incorporate audio-related image information do not improve speech recognition performance.
Approach:	They propose a novel approach utilizing audio-related image information and set up a multimodal speech recognition system that uses vision as hotwords to enhance the model’s speech recognition capability.
Outcome:	The proposed model outperforms unimodal ASR model and achieves SOTA among existing image-based multimodal ASL models.

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Zhaoyang Han, Yihe Liu, Kai Zhang, Ping Li

Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning (2020.acl-main)

Challenge:	Existing methods for jailbreak have poor transferability and high sensitivity to preprocessing . EMJO provides an effective and scalable paradigm for systematic jailbreak optimization .
Approach:	They propose a model that couples agents into a closed-loop "probe–evaluate–revise” process . they propose EMJO, which can be query-efficient and transferable, under black-box access.
Outcome:	a new approach outperforms existing jailbreak baselines on diverse LLMs . it achieves up to 11% improvement in attack success rate while reducing query cost .

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Hongliang Fei, Ping Li

Memory or Reasoning? Explore How LLMs Compute Mixed Arithmetic Expressions (2025.findings-acl)

Challenge:	Recent neural network models have achieved impressive performance on sentiment classification in English and other languages.
Approach:	They propose an unsupervised sentiment classification model that leverages an uncontrolled machine translation system and a language discriminator to learn a shared representation.
Outcome:	The proposed model outperforms other models on five language pairs.

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Chengzhi Li, Heyan Huang, Ping Jian, Zhen Yang, Chenxu Wang, Yifan Wang

Reasoning

Challenge:	Large language models (LLMs) can solve complex multi-step math reasoning problems, but their internal implementation is limited.
Approach:	They propose to use a "Causal Effect Driven F**ine-tuning method" to improve LLMs' reasoning ability.
Outcome:	The proposed method improves the model's reasoning ability by enhancing key components that are used to execute mixed arithmetic calculations.

Learning Neural Representation for CLIR with Adversarial Framework (D18-1)

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Bo Li, Ping Cheng

Enhancing Ancient Chinese Understanding with Derived Noisy Syntax Trees (2023.acl-srw)

Challenge:	Existing studies in cross-language information retrieval (CLIR) use general text representation models that are not optimized for the target task.
Approach:	They propose a novel text representation model based on adversarial learning which seeks a task-specific embedding space for CLIR.
Outcome:	The proposed model outperforms state-of-the-art continuous space models and is better than the strong machine translation baseline.

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Ping Wang, Shitou Zhang, Zuchao Li, Jingrui Hou

A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression (2020.emnlp-main)

Challenge:	Syntactic information is not used in modern Chinese understanding tasks due to the lack of syntactical annotation.
Approach:	They propose a confidence-based syntax encoding network to alleviate the side effects of unsupervised syntax derivation and the incompatibility between ancient and modern Chinese.
Outcome:	The proposed component alleviates side effects from unsupervised syntax derivation and incompatibility between ancient and modern Chinese.

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Mingming Sun, Wenyue Hua, Zoey Liu, Xin Wang, Kangjie Zheng, Ping Li

Open Information Extraction

Challenge:	Existing OIE (Open Information Extraction) algorithms are redundant and not reusable.
Approach:	They propose a pipeline where an Open-domain Information eXpression task provides a platform for all OIE strategies.
Outcome:	The proposed pipeline provides a platform for all OIE strategies.

Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts (2020.acl-main)

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Jingyuan Zhang, Mingming Sun, Yue Feng, Ping Li

Explainability & Interpretability in NLP

Challenge:	Concept graphs are created as universal taxonomies for text understanding in the open domain knowledge.
Approach:	They propose to learn interpretable relationships from open-domain facts to enrich concept graphs.
Outcome:	The proposed method improves the identification of concepts for entities based on relations between entities on public English and Chinese datasets.

Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA (2026.findings-acl)

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Zhanli Li, Yixuan Cao, Lvzhou Luo, Ping Luo

Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions (2026.findings-acl)

Challenge:	Existing multi-document QA benchmarks require information from only a few documents with limited cross-document reasoning.
Approach:	They propose a benchmark for multi-document analytical QA that extracts and synthesizes information across multiple documents to perform quantitative analysis.
Outcome:	The proposed approach improves both process and outcome metrics but still has bottlenecks compared to human experts.

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Zhongbin Guo, Zhen Yang, Yushan Li, Xinyue Zhang, Wenyu Gao, Jiacheng Wang, Chengzhi Li, Xiangrui Liu, Ping Jian

Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction (2025.naacl-long)

Challenge:	Existing advances in Spatial Intelligence rely on vision-Language Models . however, a critical question remains: does spatial understanding originate from visual encoders?
Approach:	They propose to evaluate the SI performance of Large Language Models without pixel-level input.
Outcome:	The proposed benchmark challenges large language models to perform symbolic reasoning rather than visual pattern matching.

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Lu Yang, Jiajia Li, En Ci, Lefei Zhang, Zuchao Li, Ping Wang

Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision (2026.acl-long)

Challenge:	Extractive UIEs can solve model explosion problems using a relatively small model . single-target instruction UIE enables the extraction of only one type of relation at a time .
Approach:	They propose a model that assigns different relations to different levels for understanding and decision-making.
Outcome:	Experiments show that LDNet outperforms state-of-the-art systems on 9 tasks, 33 datasets . LDnet outperformed state- of-the art systems on single-modal and multi-modal tasks .

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Bingsen Chen, Boyan Li, Ping Nie, Yuyu Zhang, Xi Ye, Chen Zhao

Challenge:	Existing benchmarks for Deep Research Agents (DRAs) treat report generation as a single-shot writing task.
Approach:	They propose an evaluation suite that establishes multi-turn report revision as a new axis.
Outcome:	The evaluation suite establishes multi-turn report revision as a new axis.

Dialogue-RAG: Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting (2025.acl-long)

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Qiwei Li, Teng Xiao, Zuchao Li, Ping Wang, Mengjia Shen, Hai Zhao

Paraphrasing

Challenge:	Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) methods have demonstrated significant potential on tasks across multiple domains.
Approach:	They propose a lightweight IUR model for query rewriting to complete key information in dialogue to enhance retrieval.
Outcome:	The proposed model improves retrieval and generation ability of RAG system in multi-round dialogue scenarios.

A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing (2023.findings-acl)

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Ye Ma, Mingming Sun, Ping Li

Structured Data in NLP

Challenge:	Existing parsers that capture dependency graphs are lacking in capturing explicit dependencies . graph-based parsing is a popular choice for capturing dependency relationships between words .
Approach:	They propose a semi-autoregressive dependency parser that generates dependency graphs by adding nodes and edge groups autoregressively while pouring out all group elements in parallel.
Outcome:	The proposed method outperforms baselines on Enhanced Universal Dependencies of multiple languages.

Large Margin Neural Language Model (D18-1)

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Jiaji Huang, Yi Li, Wei Ping, Liang Huang