Papers by Song Feng

STICKERCONV: Generating Multimodal Empathetic Responses from Scratch (2024.acl-long)

Challenge:	Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it .
Approach:	They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers.
Outcome:	The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy .

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Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, SWangLing SWangLing, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song

MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition (2021.acl-long)

Challenge:	Prior studies on stickers focused on sentiment analysis and recommendation systems, overlooking their vast potential in empathetic response generation.
Approach:	They propose a multimodal empathetic dialogue dataset, STICKERCONV, which simulates human behavior with stickers, and propose evaluative metrics based on LLM.
Outcome:	The proposed framework generates contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging e-dialog systems.

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Shuang Wu, Xiaoning Song, Zhenhua Feng

MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents (2021.emnlp-main)

Challenge:	Named Entity Recognition (NER) is a sequence tagging task that extracts named entities from unstructured text.
Approach:	They propose to integrate Chinese character features with radical-level embedding to improve Chinese NER by integrating Chinese character information.
Outcome:	The proposed method can improve Chinese Named Entity Recognition (NER) on well-known datasets.

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Song Feng, Siva Sankalp Patel, Hui Wan, Sachindra Joshi

TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)

Challenge:	Existing work treats document-grounded dialogue modeling as a machine reading comprehension task based on a single document or passage.
Approach:	They propose a task and dataset for modeling goal-oriented dialogues grounded in multiple documents.
Outcome:	The proposed task and dataset address realistic scenarios where goal-oriented dialogues involve multiple topics and hence are grounded on different documents.

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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi

BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation (2025.coling-main)

Challenge:	TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications.
Approach:	They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Outcome:	The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions.

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Minchong Li, Feng Zhou, Xiaohui Song

doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset (2020.emnlp-main)

Challenge:	Knowledge distillation (KD) is a method for reducing model size while preserving performance.
Approach:	They propose a method to distill large language models at the logit level by transferring knowledge from a large teacher model to a smaller student model.
Outcome:	The proposed method outperforms supervised fine-tuning, vanilla KL loss and five other distillation methods on 13 datasets.

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Song Feng, Hui Wan, Chulaka Gunasekara, Siva Patel, Sachindra Joshi, Luis Lastras

Chat-crowd: A Dialog-based Platform for Visual Layout Composition (N19-4)

Challenge:	doc2dial dataset is a goal-oriented document-grounded dialogue model . it is based on how the authors compose documents for guiding end users .
Approach:	They propose a dataset of goal-oriented dialogues grounded in documents . they use annotated conversations with an average of 14 turns to generate conversational utterances .
Outcome:	The proposed dataset includes over 4500 annotated conversations with an average of 14 turns grounded in over 450 documents from four domains.

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Paola Cascante-Bonilla, Xuwang Yin, Vicente Ordonez, Song Feng

Multimodality

Challenge:	We present Chat-crowd, an interactive environment for visual layout composition via conversational interactions . system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection .
Approach:	They introduce an interactive environment for visual layout composition via conversational interactions that supports multiple agents with two conversational roles.
Outcome:	The proposed system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection and has quality controls on the performance of both types of agents.

Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023.emnlp-main)

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Mingyang Song, Pengyu Xu, Yi Feng, Huafeng Liu, Liping Jing

SEARL: Joint Optimization of Policy and Tool Graph Memory for Self-Evolving Agents (2026.acl-long)

Challenge:	Existing keyphrase extraction models incorrectly determine a keyphrase as a phrase but output other candidates as keyphrases because they contain the same word.
Approach:	They propose a new approach that detects both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate keyphrase.
Outcome:	The proposed approach outperforms state-of-the-art keyphrase extraction models on three benchmark datasets.

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Xinshun Feng, Xinhao Song, Lijun Li, Gongshen Liu, Jing Shao

Multimodality

Challenge:	Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have demonstrated significant potential in single-turn reasoning tasks.
Approach:	They propose a tool-memory based self-evolving agentic framework that integrates planning with execution.
Outcome:	The proposed framework is able to extract explicit knowledge from historical data and leverage inter-trajectory correlations to densify reward signals.

A New Approach to Overgenerating and Scoring Abstractive Summaries (2021.naacl-main)

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Kaiqiang Song, Bingqing Wang, Zhe Feng, Fei Liu

Text Generation

Challenge:	Abstractive summarization is a learning objective to produce system outputs that resemble reference summaries on a word-to-word basis.
Approach:	They propose a two-staged strategy to generate multiple variants of the target summary and score and select admissible ones according to users’ needs.
Outcome:	The proposed approach can achieve state-of-the-art on benchmark summarization datasets.

D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)

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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan

Challenge:	Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction.
Approach:	They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process.
Outcome:	The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process .

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)

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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

Token-level Inference-Time Alignment for Vision-Language Models (2026.findings-acl)

Challenge:	rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research.
Approach:	They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication.
Outcome:	The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication.

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Kejia Chen, Junjun Zheng, Jiawen Zhang, Manxi Lin, Xiao Pan, Jiacong Hu, Jian Lou, Zunlei Feng, Mingli Song

MixTEA: Semi-supervised Entity Alignment with Mixture Teaching (2023.findings-emnlp)

Challenge:	Vision-Language Models (VLMs) often prioritize linguistic fluency over visual fidelity . despite widespread adoption, VLMs often exhibit a critical failure mode: hallucination .
Approach:	They propose a framework for Token-level Inference-Time Alignment that steers the decoding process without updating the base model parameters.
Outcome:	The proposed framework improves performance on 13 benchmarks across architectures . it boosts LLaVA-1.5-7B by 8.6% on MMVet and achieves a 74.0 MMStar score .

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Feng Xie, Xin Song, Xiang Zeng, Xuechen Zhao, Lei Tian, Bin Zhou, Yusong Tan

Low-Resource NLP

Challenge:	Existing methods to learn informative entity embeddings are insufficient for semi-supervised entity alignment.
Approach:	They propose a semi-supervised method which guides the model learning with an end-to-end mixture teaching of manually labeled mappings and probabilistic pseudo mappings.
Outcome:	The proposed method is superior to existing methods on benchmark datasets and further analyses.

Revealing the Parallel Multilingual Learning within Large Language Models (2024.emnlp-main)

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Yongyu Mu, Peinan Feng, Zhiquan Cao, Yuzhang Wu, Bei Li, Chenglong Wang, Tong Xiao, Kai Song, Tongran Liu, Chunliang Zhang, JingBo Zhu

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization (2024.naacl-long)

Challenge:	Large language models (LLMs) can handle multilingual and cross-lingual text within a single input; however, previous studies focusing on using English as the pivot language to enhance language understanding and reasoning focus on using multiple languages.
Approach:	They propose to use parallel multilingual input to enhance the model's comprehension of the input and to examine how multilingual processing affects prediction.
Outcome:	The proposed model can handle multilingual and cross-lingual text within a single input, but previous studies focused on using English as the pivot language to enhance language understanding and reasoning.

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Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)

Challenge:	Existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model’s size.
Approach:	They propose to evaluate topic-focused dialogue summarization by using large language models (LLMs) they use human annotations to evaluate factual consistency and explain factually inconsistent sentences.
Outcome:	The proposed evaluation benchmark on topic-focused dialogue summarization shows that existing LLMs hallucinate significant amounts of factual errors regardless of the model’s size.

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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang

Democratizing Reasoning Ability: Tailored Learning from Large Language Model (2023.emnlp-main)

Challenge:	Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed .
Approach:	They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
Outcome:	The proposed framework reduces token usage while improving accuracy on math benchmarks.

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Zhaoyang Wang, Shaohan Huang, Yuxuan Liu, Jiahai Wang, Minghui Song, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

Challenge:	Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and closed-source nature.
Approach:	They propose a tailored learning approach to distill the exclusive reasoning ability to smaller LMs to facilitate democratization.
Outcome:	The proposed approach enables the democratization of the exclusive reasoning ability by leveraging the black-box model as a reasoning teacher.

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Auto Search Indexer for End-to-End Document Retrieval (2023.findings-emnlp)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Tianchi Yang, Minghui Song, Zihan Zhang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang

Information Retrieval

Challenge:	Generative retrieval heavily relies on the “preprocessed” document identifiers, thus limiting its retrieval performance and ability to retrieve new documents.
Approach:	They propose a fully end-to-end retrieval paradigm that can learn the best docids for existing and new documents automatically via a semantic indexing module.
Outcome:	The proposed model outperforms baselines on public and industrial datasets and can handle new documents.

Scalable-DSC: A Structural Template Prompt Approach to Scalable Dialogue State Correction (2023.emnlp-main)

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Haoxiang Su, Hongyan Xie, Hao Huang, Shuangyong Song, Ruiyu Fang, Xiaomeng Huang, Sijie Feng

SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)

Challenge:	Existing approaches to correct wrong slot values in dialogue state tracking are intertwined with specific DST models, limiting their applicability to other DSTs.
Approach:	They propose a Scalable Dialogue State Correction model that corrects wrong slot values in predicted dialogue states by using a structural template prompt.
Outcome:	The proposed model achieves state-of-the-art results on MultiWOZ 2.0-2.4.

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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze

A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)

Challenge:	Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues .
Approach:	They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance .
Outcome:	The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness.

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Xiangju Li, Kaisong Song, Shi Feng, Daling Wang, Yifei Zhang

Sentiment Analysis

Challenge:	Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue.
Approach:	They propose a co-attention neural network model for emotion cause analysis with emotional context awareness.
Outcome:	The proposed model outperforms the state-of-the-art methods.

Align Attention Heads Before Merging Them: An Effective Way for Converting MHA to GQA (2025.findings-emnlp)

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Qingyun Jin, Xiaohui Song, Feng Zhou, Zengchang Qin

Challenge:	Large language models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks.
Approach:	They propose a method for converting multi-head attention into grouped-query attention with any compression ratio of KV heads.
Outcome:	The proposed method can compress up to 87.5% KV heads of LLaMA2-7B model and 75% Kv heads of Sheared-LLa MA-1.3B with acceptable performance degradation.

ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning (2025.emnlp-main)

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Changtai Zhu, Siyin Wang, Ruijun Feng, Kai Song, Xipeng Qiu

Match More, Extract Better! Hybrid Matching Model for Open Domain Web Keyphrase Extraction (2024.findings-acl)

Challenge:	Existing approaches to conversational Query Reformulation (CQR) suffer from high dependency on external supervision from annotations or large language models and insufficient alignment between the rewriter and downstream retrievers.
Approach:	They propose a framework that transforms context-dependent queries into self-contained forms suitable for off-the-shelf retrievers.
Outcome:	The proposed framework outperforms existing methods on topiOCQA and QReCC datasets while using smaller 3B parameter models without external supervision.

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Mingyang Song, Liping Jing, Yi Feng

Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation (2022.findings-naacl)

Challenge:	Existing models for keyphrase extraction use noisy information to filter the salient phrases from the document.
Approach:	They propose a hybrid matching model that combines representation-focused and interaction-based matching modules into a unified framework for improving keyphrase extraction.
Outcome:	The proposed model outperforms state-of-the-art keyphrase extraction models on the OpenKP dataset.

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Yukun Feng, Feng Li, Ziang Song, Boyuan Zheng, Philipp Koehn

REAL: REtrieval-reAsoning and Logic-constructed Attention Behaviors for Long-Context KV Cache Compression (2026.acl-long)

Challenge:	Recent studies have shown that the effective use of contextual information between sentences can achieve better performance in document-level machine translation.
Approach:	They propose a recurrent memory unit to the Transformer to support the information exchange between the sentence and previous context.
Outcome:	The proposed model outperforms the previous work on TED and News by 0.91 s-BLEU and 1.49 d-BLUE on average.

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Mengjie Li, Yuan Feng, Xike Xie, William J. Song

InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles (2025.emnlp-main)

Challenge:	Existing methods to evict keyvalue caches ignore diverse behavior in failure cases, such as bias and distraction.
Approach:	They propose a method to analyze attention head behaviors in success and failure scenarios by maximizing signal-to-noise ratio and minimizing noise from bias and distraction.
Outcome:	The proposed method achieves comparable accuracy to the strongest baseline, HeadKV-R2 on LongBench v2 while requiring 32x less space.

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Zizhen Li, Chuanhao Li, Yibin Wang, Qi Chen, Diping Song, Yukang Feng, Jianwen Sun, Jiaxin Ai, Fanrui Zhang, Mingzhu Sun, Kaipeng Zhang

DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)

Challenge:	Recent large language models (LLMs) have demonstrated strong reasoning abilities across complex mathematical and scientific domains.
Approach:	They propose a framework to assess whether LLMs can capture and apply personalized reasoning styles in social deduction games.
Outcome:	The proposed framework evaluates LLMs on the game Avalon and shows that they can capture and apply individualized reasoning styles.

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Wei Fan, Wenlin Yao, Zheng Li, Feng Yao, Xin Liu, Liang Qiu, Qingyu Yin, Yangqiu Song, Bing Yin

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

Challenge:	Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage.
Approach:	They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents.
Outcome:	Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget.

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

World Knowledge for Abstract Meaning Representation Parsing (L18-1)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Charles Welch, Jonathan K. Kummerfeld, Song Feng, Rada Mihalcea

Does Structure Matter? Encoding Documents for Machine Reading Comprehension (2021.naacl-main)

Challenge:	Abstract Meaning Representation (AMR) parsers are based on annotated graphs, but there is still room for improvement .
Approach:	They examine the role played by world knowledge in parsing errors in a state-of-the-art parser . they examine the effects of different types of world knowledge on parsers .
Outcome:	The proposed model improves on multiple fine-grained metrics, including a 6% increase in named entity F-score, and provides insight into the potential of world knowledge for future work in Abstract Meaning Representation parsing.

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Hui Wan, Song Feng, Chulaka Gunasekara, Siva Sankalp Patel, Sachindra Joshi, Luis Lastras

Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020.acl-main)

Challenge:	Existing Transformer-based models for machine reading comprehension treat documents as flat sequences.
Approach:	They propose a Transformer-based method that reads a document as tree slices and jointly trains and consults the modules at inference time.
Outcome:	The proposed method outperforms several baseline approaches on two datasets from varied domains.

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Najoung Kim, Song Feng, Chulaka Gunasekara, Luis Lastras

Text Classification

Challenge:	Lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in literature.
Approach:	They propose an improved evaluation protocol for implicit relation classification on PDTB 2.0 . they report strong baseline results from pretrained sentence encoders .
Outcome:	The proposed evaluation protocol improves the existing framework and provides strong baseline results.

AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation (2025.findings-emnlp)

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Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, Guochao Jiang, Jingyi Song

ResLoRA: Identity Residual Mapping in Low-Rank Adaption (2024.findings-acl)

Challenge:	Experimental results show the effectiveness of AirRAG on complex question-answering datasets.
Approach:	They propose a new thinking pattern that integrates autonomous strategic planning with efficient reasoning actions.
Outcome:	The proposed approach significantly activates intrinsic reasoning capabilities and expands the solution space of specific tasks via Monte Carlo Tree Search.

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Shuhua Shi, Shaohan Huang, Minghui Song, Zhoujun Li, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

Challenge:	Low-rank adaptation (LoRA) is one of the most popular parameter-efficient fine-tuning methods.
Approach:	They propose a low-rank adaptation method that adds residual paths during training and merges them together during inference to achieve better results.
Outcome:	The proposed method achieves 2.5x faster convergence speed and improves performance by 14.3% on NLG, NLU, and text-to-image tasks.

Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors (2025.acl-long)

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Senqi Yang, Dongyu Zhang, Jing Ren, Ziqi Xu, Xiuzhen Zhang, Yiliao Song, Hongfei Lin, Feng Xia

Sentiment Analysis

Challenge:	Metaphors are pervasive in communication, making them crucial for natural language processing.
Approach:	They propose a multicultural multimodal metaphor dataset designed for cross-cultural studies of metaphor in Chinese and English.
Outcome:	The proposed model improves metaphor comprehension across cultural backgrounds and cultural domains.

Hyperbolic Relevance Matching for Neural Keyphrase Extraction (2022.naacl-main)

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Mingyang Song, Yi Feng, Liping Jing

DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting (2025.findings-emnlp)

Challenge:	Keyphrase extraction is a fundamental task in natural language processing that aims to extract a set of phrases with important information from a source document.
Approach:	They propose a hyperbolic matching model to explore keyphrase extraction in hyperbolical space using word embeddings from RoBERTa to capture hierarchical syntactic and semantic structures.
Outcome:	The proposed model outperforms the state-of-the-art models on six benchmark datasets and outperformed previous models.

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Mingchen Li, Heng Fan, Song Fu, Junhua Ding, Yunhe Feng

ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models (2024.acl-long)

Challenge:	Existing methods for prompt privacy focus on document-level rewriting, neglecting rich, multi-granular representations of text.
Approach:	a framework that leverages local differential privacy and composition theorem via group text rewriting is proposed . the framework is compatible with existing rewrite techniques and is publicly available at anonymous.4open.science for reproducibility.
Outcome:	DP-GTR is the first framework to integrate document-level and word-level information while exploiting in-context learning to improve privacy and utility.

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Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, Linqi Song

PhonoThink: Improving Large Language Models’ Reasoning on Chinese Phonological Ambiguities (2025.emnlp-main)

Challenge:	Existing evaluation methods for large language models are labor-intensive and lack efficiency.
Approach:	They propose a framework dedicated to assessing long-text generation that includes in-depth human-curated meta-questions spanning various domains . they use a set of proxy-quests with pre-annotated answers to assess the content's quality by incorporating the generated texts as contextual background.
Outcome:	The proposed framework assesses the quality of long-text content by matching it with references through human evaluation or automated metrics.

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Jianfei Ma, Zhaoxin Feng, Emmanuele Chersoni, Huacheng Song, Ziqi Zhang

CMB: A Comprehensive Medical Benchmark in Chinese (2024.naacl-long)

Challenge:	Effectively resolving phonological ambiguities is crucial for robust natural language processing, as these ambiguity are pervasive in tasks ranging from speech-to-text, spelling correction, to offensive language detection.
Approach:	They propose a framework to enhance LLMs’ phonological capability through a multiple-stage training approach.
Outcome:	The proposed framework enables the base model to achieve comparable performance to a much larger model.

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Xidong Wang, Guiming Chen, Song Dingjie, Zhang Zhiyi, Zhihong Chen, Qingying Xiao, Junying Chen, Feng Jiang, Jianquan Li, Xiang Wan, Benyou Wang, Haizhou Li

A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (2023.findings-eacl)

Challenge:	Large Language Models (LLMs) provide a great breakthrough in medicine, says a new study . existing studies on LLMs leverage subjective evaluation, but evaluation in medicine is professional .
Approach:	They propose a localized medical benchmark in Chinese rooted in native Chinese . they propose to use traditional Chinese medicine to evaluate large-scale LLMs .
Outcome:	a new benchmark is developed to evaluate large-scale LLMs in china . the proposed model is rooted in the native Chinese linguistic and cultural framework .

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Mingyang Song, Yi Feng, Liping Jing

Improving Embedding-based Unsupervised Keyphrase Extraction by Incorporating Structural Information (2023.findings-acl)

Challenge:	Keyphrase extraction is a key component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the source document.
Approach:	They propose to use supervised and unsupervised keyphrase extraction techniques to investigate the state-of-the-art models for keyphrase extracting.
Outcome:	The proposed keyphrase extraction system can significantly accelerate the speed of retrieval and help people get first-hand information from a long document quickly and accurately.

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Mingyang Song, Huafeng Liu, Yi Feng, Liping Jing

Language Models as Continuous Self-Evolving Data Engineers (2025.emnlp-main)

Challenge:	Existing unsupervised keyphrase extraction models ignore the indicative role of the highlights in certain locations, leading to wrong keyphrases extraction.
Approach:	They propose a Highlight-Guided Unsupervised Keyphrase Extraction model that models phrase-document relevance via the highlights of documents and calculates cross-phrase relevance between all candidate phrases.
Outcome:	The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction models on three benchmarks.

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Peidong Wang, Ming Wang, Zhiming Ma, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song

Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slips (2025.coling-main)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their further evolution is often hampered by the scarcity of high-quality training data and the heavy reliance of traditional methods on expert-labeled data.
Approach:	They propose a paradigm that enables LLMs to train themselves by generating, cleaning, reviewing and annotating data with preference information.
Outcome:	The proposed model can generate, clean, review, and annotate data with preference information significantly reducing time and cost of post-training data construction.

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Yingfa Chen, Chenlong Hu, Cong Feng, Chenyang Song, Shi Yu, Xu Han, Zhiyuan Liu, Maosong Sun

Syntactic Text Processing

Challenge:	Using a multi-modal multi-granularity tokenizer, we analyze ancient Chinese scripts . a large proportion of the characters in ancient Chinese are rare or undeciphered .
Approach:	They propose a multi-modal multi-granularity tokenizer specifically designed for ancient Chinese scripts.
Outcome:	The proposed tokenizer improves on the part-of-speech tagging task on the Chu bamboo slip script.

Lock on Target! Precision Unlearning via Directional Control (2025.findings-emnlp)

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Yuntao Wen, Ruixiang Feng, Feng Guo, Yifan Wang, Ran Le, Yang Song, Shen Gao, Shuo Shang

Challenge:	Existing methods for unlearning harmful, sensitive, or outdated knowledge suffer from two critical limitations: (1) collateral forgetting, where erasing target data inadvertently removes related but desirable knowledge, and (2) generality forgetting degrades the model’s general capabilities.
Approach:	They propose a method that identifies and leverages a targeted "unlearning direction" in the model's parameter space and selectively updates along this direction.
Outcome:	Experiments show that the proposed method achieves state-of-the-art unlearning precision while preserving both related knowledge and general capabilities.

SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning (2025.emnlp-main)

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Kaiwen Zhou, Xuandong Zhao, Jayanth Srinivasa, Gaowen Liu, Aosong Feng, Dawn Song, Xin Eric Wang

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks (2021.naacl-main)

Challenge:	Large Reasoning Models (LRMs) introduce a new paradigm of explicitly reasoning before answering, but they pose great safety risks against harmful queries and adversarial attacks.
Approach:	They propose a safety aha moment that activates safety reasoning and leads to a safe response.
Outcome:	The proposed model can generalize to unseen jailbreak prompts while maintaining general abilities.

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Hanjie Chen, Song Feng, Jatin Ganhotra, Hui Wan, Chulaka Gunasekara, Sachindra Joshi, Yangfeng Ji

Cat-MoD: Accelerating Multimodal Alignment via Caption Token Guided Asymmetric Mixture-of-Depths (2026.acl-long)

Challenge:	Existing methods to explain neural network models are computationally inefficient for text inputs.
Approach:	They propose a method to implicitly detect word correlations by grouping correlated words from input text pairs together and measuring their contribution to corresponding NLP tasks.
Outcome:	The proposed method is evaluated with two different model architectures across four datasets.

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YiJie Huang, Xiaocui Yang, Shi Feng, Wen Zhang, Kaisong Song, Yifei Zhang, Daling Wang

Multimodality

Challenge:	Existing query-based alignment modules enforce uniform cross-attention across all layers, leading to computational redundancy.
Approach:	They propose a framework that allows for asynchronous query-based alignment with large-scale visual features.
Outcome:	The proposed framework matches or surpasses baseline performance while reducing alignment FLOPs by approximately 37% during training and inference.

Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network (P19-1)

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Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu

Structured Data in NLP

Challenge:	Existing approaches to cross-lingual knowledge graph (KG) alignment rely on entity embeddings derived from monolingual KG structural information.
Approach:	They propose a topic entity graph to represent entities with contextual information in KGs.
Outcome:	The proposed model outperforms state-of-the-art methods by a large margin.

CogCompNLP: Your Swiss Army Knife for NLP (L18-1)

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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth

Challenge:	a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks.
Approach:	They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community .
Outcome:	The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges.

Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning (2020.emnlp-main)

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Ye Liu, Sheng Zhang, Rui Song, Suo Feng, Yanghua Xiao

Structured List-Grounded Question Answering (2025.coling-main)

Challenge:	Existing methods for open attribute value extraction for emerging entities are noisy or incomplete, even missing.
Approach:	They propose a knowledge-guided reinforcement learning framework for open attribute value extraction for emerging entities.
Outcome:	The proposed framework outperforms baselines by 16.5 - 27.8%.

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Mujeen Sung, Song Feng, James Gung, Raphael Shu, Yi Zhang, Saab Mansour