Papers by Jianjun Li
STREAM-ZH: Simplified Topic Retrieval Exploration and Analysis Module for Chinese Language (2026.eacl-short)
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| Challenge: | Simplified Topic Retrieval Exploration and Analysis Module for Chinese language is the first topic modeling package to fully support the Chinese language. |
| Approach: | They propose a topic modeling package that fully supports the Chinese language . they use preprocessed textual datasets to assess topic models . |
| Outcome: | The proposed framework outperforms existing topic models using English-translated textual input. |
I2E: From Image Pixels to Actionable Interactive Environments for Text-Guided Image Editing (2026.acl-long)
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Jinghan Yu, Junhao Xiao, Chenyu Zhu, Jiaming Li, Jia Li, HanMing Deng, Xirui Wang, Guoli Jia, Jianjun Li, Xiang Bai, Bowen Zhou, Zhiyuan Ma
| Challenge: | Existing text-guided image editing methods rely on end-to-end pixel-level inpainting paradigm . existing models lack such intermediate representations and Reasoning-then-action process . |
| Approach: | They propose a "Decompose-then-Action" paradigm that revisits image editing as an actionable interaction process within a structured environment. |
| Outcome: | The proposed paradigm outperforms existing methods in compositional editing tasks. |
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification (2022.coling-1)
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| Challenge: | Existing fact verification models lack fine-grained reasoning over key entities . GLAF uses local fission reasoning to capture latent logical relations between clues . |
| Approach: | They propose a global-to-local fission and fissional network to capture latent logical relations hidden in multiple evidence clues. |
| Outcome: | The proposed network achieves state-of-the-art on a FEVER dataset with a 77.62% FEVER score. |
GASE: Graph-Aware Semantic Embedding Learning with Frozen LLMs for Text-Attributed Graphs (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown strong potential for text-attributed graph (TAG) learning, yet effectively integrating LLM semantics with graph structural information remains challenging. |
| Approach: | They propose a framework for learning Graph-Aware Semantic Embeddings using frozen LLMs. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on node classification and achieves a 5 speedup over fine-tuning-based methods. |
UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System (2022.acl-long)
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| Challenge: | Existing studies on multimodal task-oriented dialog systems follow the pipeline to learn intra-modal features separately and then conduct simple feature concatenation or attention-based feature fusion to generate responses. |
| Approach: | They propose a Unified Transformer Semantic Representation framework with feature alignment and intention reasoning for multimodal dialog systems that embed multimodal features into a unified Transformer semantic space to prompt inter-modal interactions. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art approaches on the representative MMD dataset. |
Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue (2021.emnlp-main)
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| Challenge: | Recent years has witnessed the remarkable success in end-to-end task-oriented dialog system, especially when incorporating external knowledge information. |
| Approach: | They propose a mechanism to model deterministic entity knowledge by using an intention reasoning network to obtain intention-aware representations of conceptual tokens. |
| Outcome: | The proposed mechanism captures concept shifts and generates accurate responses on two representative multi-domain dialog datasets. |
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)
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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li
| Challenge: | Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) . |
| Approach: | They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries. |
| Outcome: | The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. |
MARD: Module-Aware Reasoning Distillation for Language Models with Adaptive Supervision (2026.acl-long)
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| Challenge: | Multi-step reasoning remains challenging for language models with limited capacity . et al., 2025) demonstrate remarkable reasoning capabilities across diverse tasks . |
| Approach: | They propose a module-aware reasoning distillation framework that explicitly targets key Transformer components for effective reasoning transfer. |
| Outcome: | The proposed framework targets key components for effective reasoning transfer . it adopts an offline distillation setting, where a strong teacher model provides reasoning trajectories in advance . |