Papers by Song Fu
IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation (2021.acl-demo)
Copied to clipboard
| Challenge: | Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way. |
| Approach: | They propose to use Chinese AEA system IFlyEssayAssess to evaluate essays written by native Chinese students from primary and junior schools. |
| Outcome: | The proposed system provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization. |
Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax (2026.findings-acl)
Copied to clipboard
Zeli Su, Ziyin Zhang, Zhou Liu, Xuexian Song, Zhankai Xu, Longfei Zheng, Xiaolu Zhang, Rong Fu, Guixian Xu, Wentao Zhang
| Challenge: | Extending large language models to low-resource languages often incurs an "alignment tax" token-level fine-tuning enforces token-level surface imitation on narrow and biased data distributions. |
| Approach: | They propose a semantic-space alignment paradigm powered by group-level semantic rewards instead of likelihood maximization. |
| Outcome: | The proposed model acquires low-resource capa- bilities while mitigating alignment tax on Tibetan–Chinese machine translation and Ti- betan headline generation. |
Multi-Stage Pre-training for Automated Chinese Essay Scoring (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for automatic essay scoring are based on hand-crafted surface-level features, but recent advances in representation learning have improved performance. |
| Approach: | They propose a pre-training based automated Chinese essay scoring method with weakly supervised pre- training, supervised cross- prompt fine-tuning and supervised target- prompt refine-tuneing. |
| Outcome: | The proposed method improves a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations. |
Cognitive Scaffold: From Fluid Context to Crystallized Memory for Long-Horizon DeepResearch Agents (2026.acl-long)
Copied to clipboard
| 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. |
End-to-End AMR Coreference Resolution (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work on AMR focuses on individual sentences, but there is a need for multi-sentence AMRs. |
| Approach: | They propose to use an end-to-end AMR coreference resolution model to generate multi-sentence AMRs. |
| Outcome: | The proposed model reduces error propagation and is more robust for both in- and out-domain situations. |
SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)
Copied to clipboard
Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni
| Challenge: | Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging . |
| Approach: | They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks. |
| Outcome: | The proposed framework bridges the domain gap between LLMs and recommendation tasks. |
Verb Metaphor Detection via Contextual Relation Learning (2021.acl-long)
Copied to clipboard
| Challenge: | Recent work on verb metaphor detection focuses on analyzing restricted forms of linguistic context. |
| Approach: | They propose a model which explicitly models the relation between a verb and its various contexts. |
| Outcome: | The proposed model gets competitive results compared with state-of-the-art approaches on the VUA, MOH-X and TroFi datasets. |
Towards Understanding Omission in Dialogue Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for dialogue summarization are far from satisfactory . omission is a major factor in affecting the quality of summarizing, but few studies have explored the problem . |
| Approach: | They propose a dataset that provides high-quality omission labels for dialogue summarization . they propose to use this dataset to detect omitted dialogue utterances . |
| Outcome: | The proposed dataset improves summarization quality by providing ground-truth omission labels . the proposed dataset and codes are publicly available . |
Neural Multitask Learning for Simile Recognition (D18-1)
Copied to clipboard
| Challenge: | Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. |
| Approach: | They propose a neural network framework for simile sentence classification, simile component extraction and language modeling. |
| Outcome: | The proposed framework outperforms rule-based and feature-based approaches in simile sentence classification and simile component extraction tasks. |
MARIO-0.5B: A Multi-Agent Lightweight Model for Real-Time Open Information Extraction in Low-Resource Settings (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models have shown remarkable capabilities in open information extraction, but their resource requirements often restrict their deployment in resource-constrained industrial settings. |
| Approach: | They introduce an ultra-lightweight large language model trained on instruction-based samples in Chinese, English, Korean, and Russian. |
| Outcome: | The proposed model outperforms large-scale models with up to 70B parameters, reducing computational resources by 140x and delivering 11x faster response times. |
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems (2025.emnlp-industry)
Copied to clipboard
Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song, Yun Dai, Aman Gupta, Zhipeng Wang, Hejian Sang, Shao Tang, Gregory Dexter, Sirou Zhu, Siyu Zhu, Tejas Dharamsi, Vignesh Kothapalli, Zhoutong Fu, Yihan Cao, Pin-Lun Hsu, Fedor Borisyuk, Natesh S. Pillai, Luke Simon, Rahul Mazumder
| Challenge: | Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications. |
| Approach: | They propose two techniques for training and deploying small language models that deliver high performance for a variety of industry use cases. |
| Outcome: | The proposed techniques retain much of the quality of larger models while reducing training/serving costs and latency. |
ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA (2025.emnlp-main)
Copied to clipboard
Zhao Xinjie, Fan Gao, Xingyu Song, Yingjian Chen, Rui Yang, Yanran Fu, Yuyang Wang, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Multi-hop question answering (QA) is a central challenge in natural language processing . early mistakes can cause errors and undermine the final result, authors say . |
| Approach: | They propose a reversible multi-agent reasoning framework that backtracks to earlier valid states when conflicts arise. |
| Outcome: | Empirical evaluation shows that the framework improves on forward-only benchmarks by 6% . the approach enables agents to backtrack to valid states when conflicts arise . |
Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies (2025.naacl-long)
Copied to clipboard
Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Fu Yujie, Zeyu Zhang, Shiyu Jiang, Miao Fang, Ling Chen, Xiuying Chen
| Challenge: | Existing household robots are inability to recognize potential problems or dangers in home environments. |
| Approach: | They propose a task of creating anomaly scenarios using generative models instead of manually labeled data to build simulated environments. |
| Outcome: | The proposed framework outperforms existing models in terms of task description and scene diversity. |
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | In-context learning (ICL) enables large language models to perform novel tasks without parameter updates by conditioning on a few input-output examples. |
| Approach: | They propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling. |
| Outcome: | The proposed pipeline reduces reliance on LLMs for data labeling . it leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances. |
DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting (2025.findings-emnlp)
Copied to clipboard
| 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. |
From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for drug repurposing ignore common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. |
| Approach: | They propose a Large Language Model-assisted framework for Drug Repurposing which improves the representation of biomedical concepts within KGs. |
| Outcome: | The proposed framework improves the representation of biomedical concepts within KGs by extracting treatment-related textual representations of biomedic entities from large language models and fine-tuning knowledge graph embedding models. |
Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays (2020.emnlp-main)
Copied to clipboard
| Challenge: | Despite its importance, discourse element identification is challenging due to the ambiguity of sentences . the number of elaboration sentences could be 10 times more than the number edna sentences. |
| Approach: | They propose to use sentence positional encodings to explicitly represent sentence positions and inter-sentence attentions to capture sentence interactions and enhance sentence representation. |
| Outcome: | The proposed model improves on a Chinese and English dataset. |
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)
Copied to clipboard
Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang
| Challenge: | Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence. |
| Approach: | They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included . |
| Outcome: | The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model. |
Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models for customer satisfaction prediction (CSP) focus on analyzing subjective customer satisfaction in conversational service, but they are hard to represent the important dynamic satisfaction states throughout the customer journey. |
| Approach: | They propose a model to track customer satisfaction in chatbots using a dialogue-level classification module to represent the dynamic satisfaction states at each turn. |
| Outcome: | The proposed model outperforms baselines and shows that it significantly outperformed multiple baselines. |
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Knowledge-Based Visual Question Answering lack multimodal retrieval . large language models (LLMs) have demonstrated remarkable generalization and reasoning capabilities in text-based systems. |
| Approach: | They propose a multimodal vision-language retrieval-augmented generation system that harmonizes multiple modalities and modality to enhance retrieval. |
| Outcome: | The proposed system achieves state-of-the-art retrieval performance and competitive answers on InfoSeek and Encyclopedic-VQA benchmarks. |