Papers by Jintao Li
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View (2022.coling-1)
Copied to clipboard
| Challenge: | Multi-Document Scientific Summarization (MDSS) aims to produce concise and concise summaries for clusters of topic-relevant scientific papers. |
| Approach: | They propose a model that incorporates knowledge graphs into paper encoding and decoding processes and propose 'decoder' for generating knowledge graph information of summary in the form of descriptive sentences. |
| Outcome: | The proposed architecture improves on baselines on the Multi-Xscience dataset. |
Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)
Copied to clipboard
Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation. |
| Approach: | They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved. |
| Outcome: | The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment. |
Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)
Copied to clipboard
Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi
| Challenge: | Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology. |
| Approach: | They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement . |
| Outcome: | The proposed method outperforms the state-of-the-art models on three benchmarks. |
AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation (2026.acl-long)
Copied to clipboard
Rui Qian, Chuanhang Deng, Qiang Huang, Jian Xiong, Mingxuan Li, Yingbo Zhou, Wei Zhai, Jintao Chen, Dejing Dou
| Challenge: | Existing models rely on a single segmentation token whose hidden state implicitly encodes both semantic reasoning and spatial localization . Existing methods rely only on SEG>, which encodes semantic reasoning, limiting the model's ability to explicitly disentangle what to segment from where to segment. |
| Approach: | They propose a method which reformulates reasoning segmentation as a structured conditional generation process over image tokens conditioned on language grounded query banks. |
| Outcome: | The proposed model bridges token-level predictions and pixel-level supervision by decoupling spatial grounding from semantic reasoning through structured language grounded query banks. |
PAD: A Robustness Enhancement Ensemble Method via Promoting Attention Diversity (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to enhance robustness of deep neural networks focus on perturbation . weak robustness is a problem for many types of adversarial attacks, authors say . |
| Approach: | They propose a lightweight framework for enhancing robustness by perturbing parameters of a model and diversifying adversarial example distributions among different models. |
| Outcome: | The proposed method can improve robustness against adversarial attacks while maintaining accuracy on clean data. |
GOME: Grounding-based Metaphor Binding With Conceptual Elaboration For Figurative Language Illustration (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing Large Language Models (LLMs) and multimodal models are unable to illustrate figurative language based on literal objects, ignoring the underlying groundings and associations across disparate metaphorical domains. |
| Approach: | They propose a grounding-based method for metaphor illustration that integrates metaphorical knowledge into systematic instructions for existing large language models. |
| Outcome: | The proposed method is superior to existing LLMs, diffusion models, or their direct collaboration. |
Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)
Copied to clipboard
Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao
| Challenge: | Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video. |
| Approach: | They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights. |
| Outcome: | The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio. |
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)
Copied to clipboard
Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang
| Challenge: | Large Language Models (LLMs) can solve complex tasks through iterative information retrieval. |
| Approach: | They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards . |
| Outcome: | Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models. |
Event Causality Extraction via Implicit Cause-Effect Interactions (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have not exploited the interactions between the cause and effect event that could provide crucial clues for causality reasoning. |
| Approach: | They propose an Implicit Cause-Effect interaction framework which captures the implicit intra- and inter-event interactions by incorporating the privileged information for reasoning. |
| Outcome: | The proposed framework captures the implicit intra- and inter-event interactions by incorporating the privileged information (ground truth event types and arguments) for reasoning. |
Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing concepts-based explainable approaches do not discover unseen concepts . a recent approach to solve this problem is concept-based explanations . |
| Approach: | They propose a framework that extracts comprehensible concepts automatically with no annotations . ECO-Concept uses an object-centric architecture to extract task-specific semantic concepts . |
| Outcome: | a new framework extracts comprehensible concepts with no concept annotations . the proposed framework outperforms existing methods in computability tests on diverse tasks . |
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer (2022.coling-1)
Copied to clipboard
| Challenge: | Social media spreads both real news and fake news in various domains including politics, health, entertainment, etc. |
| Approach: | They propose a Domain- and Instance-level Transfer Framework for Fake News Detection which could improve the performance of specific target domains. |
| Outcome: | The proposed framework improves performance of target domains by hurting other domains, resulting in unsatisfactory performance in the target domain. |
Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper. |
| Approach: | They propose a task of Recommending Missed Citations Identified by Reviewers to help improve citations of full papers. |
| Outcome: | The proposed framework outperforms existing methods in all metrics and will motivate future research on this challenging task. |
Anchoring the Affective Manifold: Learning Canonical and Disentangled Representations via Generative Cross-Modal Alignment (2026.acl-long)
Copied to clipboard
| Challenge: | Dominant multimodal emotion recognition paradigms neglect the intrinsic geometric structure of affect, resulting in representations heavily entangled with non-affective factors. |
| Approach: | They propose a Canonical Disentangled Multimodal Generative Framework that decomposes the latent space into a canonical Shared Affective Subspace and a private Modality Subspace. |
| Outcome: | The proposed model disentangles affect from private attributes while enabling controllable emotion generation. |