Papers by Manling Li
Training-free Deep Concept Injection Enables Language Models for Video Question Answering (2024.emnlp-main)
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| Challenge: | Existing methods to train pretrained language models for zero-shot crossmodal tasks require crossmodal pretraining. |
| Approach: | They propose to inject visual concepts into the input text embedding space of a pretrained language model and build adaptation layers based on the intermediate representation of concepts. |
| Outcome: | The proposed model performs zero-shot crossmodal tasks without crossmodal pretraining . it is based on the injection of visual concepts as input tokens and augmentation in intermediate features . the proposed model achieves competitive or even better results in zero- shot and fine-tuning settings . |
Cross-media Structured Common Space for Multimedia Event Extraction (2020.acl-main)
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| Challenge: | We propose a new task to extract events and their arguments from multimedia documents . traditional methods target text, images or videos, but multimedia content is distributed via multimedia . |
| Approach: | They propose a method that encodes structured representations of semantic information from textual and visual data into a common embedding space. |
| Outcome: | The proposed method achieves 4.0% and 9.8% absolute gains on text event argument role labeling and visual event extraction. |
Joint Multimedia Event Extraction from Video and Article (2021.findings-emnlp)
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Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, Shih-Fu Chang
| Challenge: | Existing methods to extract multimedia events from video and text are limited to video and images. |
| Approach: | They propose a task to jointly extract events from video and text documents . they propose 'self-supervised' cross-modal event coreference model and cross-mod transformer architecture . |
| Outcome: | The proposed method achieves 6.0% and 5.8% absolute F-score gain on video-article pairs . the proposed method can resolve coreference and extract multimodal event frames more accurately than existing methods. |
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction (2021.emnlp-main)
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| Challenge: | Event schemas encode knowledge of stereotypical structures of events and their connections . previous work on event schema induction focuses on atomic events or linear temporal sequences . |
| Approach: | They propose a Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. |
| Outcome: | The proposed model outperforms existing models on HITS@1 by 17.8%. |
Unifying Inference-Time Planning Language Generation (2026.findings-acl)
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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang
| Challenge: | Large language models (LLMs) are used to generate a formal representation of a plan in a planning language. |
| Approach: | They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning. |
| Outcome: | The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages. |
Why Does New Knowledge Create Messy Ripple Effects in LLMs? (2024.emnlp-main)
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| Challenge: | Existing research has focused on post-training knowledge editing (KE) for language models to ensure that knowledge remains accurate and up-to-date. |
| Approach: | They propose to use a GradSim indicator to detect when and why updated knowledge ripples in language models. |
| Outcome: | The proposed indicator GradSim shows that LMs that fail to handle ripple effects have low GradSIM. |
Event-Centric Natural Language Processing (2021.acl-tutorials)
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| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
| Approach: | This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks. |
| Outcome: | This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks. |
EMCompress: Video-LLMs with Endomorphic Multimodal Compression (2026.findings-acl)
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| Challenge: | Static, sparse frame sampling either dilutes evidence across task-irrelevant segments at significant cost or misses fine-grained temporal semantics altogether. |
| Approach: | They propose a novel task that compresses multimodal input while preserving answer invariance across reasonable downstream models. |
| Outcome: | The proposed task surpasses prior methods by 0.40 F-1 with competitive query rewriting. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026.acl-long)
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Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang
| Challenge: | Tool-calling agents are increasingly deployed in real-world customer-facing workflows . but most studies on tool-callers focus on idealized settings with general, fixed, and well-specified tasks. |
| Approach: | They propose a tool-calling agent-based data pipeline that converts trajectories into user-facing tasks with controlled intent adaptations. |
| Outcome: | The proposed pipeline can be used to study tool use under three scenarios. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)
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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
| Challenge: | a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications . |
| Approach: | a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19. |
| Outcome: | a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing . |
Event Schema Induction with Double Graph Autoencoders (2022.naacl-main)
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| Challenge: | Experimental results show that a new method for learning event schemas from historical events is effective. |
| Approach: | They propose a new event schema induction framework which captures global dependencies among nodes in event graphs. |
| Outcome: | Experimental results show that the proposed model can learn event schemas with global consistency. |
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)
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Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
| Challenge: | a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation. |
| Approach: | They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements . |
| Outcome: | The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements . |
Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization (P19-1)
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| Challenge: | Existing models for extractive summarization of meetings are unfocused and lack content coverage. |
| Approach: | They propose a multi-modal hierarchical attention model that prioritizes segmentation and summarization . they propose to use multi-level hierarchies to narrow down the focus into topically-relevant segments . |
| Outcome: | The proposed model outperforms the state-of-the-art with BLEU and ROUGE measures. |
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (2020.emnlp-main)
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| Challenge: | Existing methods to automate event extraction focus on uncertainty, re-occurring events and multiple hypotheses. |
| Approach: | They propose a new Event Graph Schema where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. |
| Outcome: | The proposed model is highly effective at inducing salient and coherent schemas. |
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport (2021.emnlp-main)
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| Challenge: | Existing methods for timeline summarization ignore the events’ intra-structures and inter-structure connections. |
| Approach: | They propose to represent news articles as an event-graph, thus compressing the whole graph to its salient sub-graph. |
| Outcome: | The proposed method significantly improves on the state-of-the-art on three real-world datasets, including two public benchmarks and a Timeline100 dataset. |
A Language-First Approach for Procedure Planning (2023.findings-acl)
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| Challenge: | Developing intelligent agents requires the ability to produce plans on the fly based on visual observations. |
| Approach: | They propose a language-first procedure planning framework with a modularized design . they first align current and goal observations with corresponding steps and then use a pre-trained LM to predict intermediate steps. |
| Outcome: | The proposed framework matches state-of-the-art procedures on COIN and CrossTask benchmarks. |
Defining a New NLP Playground (2023.findings-emnlp)
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Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, Heng Ji
| Challenge: | Recent explosion of performance of large language models (LLMs) has changed the field more abruptly and seismically than any other shift in the field’s 80 year history. |
| Approach: | They propose 20+ PhD-dissertation-worthy research directions to define a new NLP playground by combining theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
| Outcome: | The proposed research will cover theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
New Frontiers of Information Extraction (2022.naacl-tutorials)
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| Challenge: | Information extraction (IE) is the process of automatically extracting structural information from unstructured or semi-structured data. |
| Approach: | This tutorial will provide an introduction to recent advances in IE by answering several important research questions. |
| Outcome: | The tutorial will address several important research questions and outline directions for further investigation. |
Foundation Models Meet Embodied Agents (2025.naacl-tutorial)
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| Challenge: | This tutorial will present a systematic overview of recent advances in foundation models for embodied agents . |
| Approach: | This tutorial will present a systematic overview of recent advances in foundation models for embodied agents. |
| Outcome: | This tutorial covers three types of foundation models for embodied agents . |
LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World (2025.findings-acl)
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Sina Semnani, Pingyue Zhang, Wanyue Zhai, Haozhuo Li, Ryan Beauchamp, Trey Billing, Katayoun Kishi, Manling Li, Monica Lam
| Challenge: | Using a partially reannotated subset of the Armed Conflict Location & Event Data, we analyze 39,786 conflict events across 20 languages and 171 countries. |
| Approach: | They propose a large-scale conflict event dataset with extensive coverage of region-specific entities. |
| Outcome: | The proposed method detects event arguments and entities through holistic document understanding and normalizes them across the multilingual dataset. |
GAIA: A Fine-grained Multimedia Knowledge Extraction System (2020.acl-demos)
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Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman
| Challenge: | Open source knowledge extraction tools are used for many real-world applications, but there is no comprehensive system for KE. |
| Approach: | They propose a multimedia knowledge extraction system that takes multimedia data from various sources and languages as input and creates a coherent, structured knowledge base. |
| Outcome: | The system achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. |
ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation (2023.emnlp-main)
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| Challenge: | State-of-the-art vision-language models have limited performance in structural knowledge extraction, such as relations between objects. |
| Approach: | They propose to leverage the inherent structure of programming language to depict visual structural information in a well-organized structured format. |
| Outcome: | The proposed framework improves visual structural knowledge extraction on visual structure prediction tasks. |
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification (2023.acl-long)
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| Challenge: | Recent methods for event schema induction use information extraction systems to construct event graph instances from documents . compared to the previous state-of-the-art closed-domain schema inducing model, human assessors were able to cover 10% more events when translating the schemas into coherent stories . |
| Approach: | They propose to treat event schemas as commonsense knowledge that can be derived from large language models. |
| Outcome: | The proposed method simplifies the schema induction process and improves readability. |
Multimedia Generative Script Learning for Task Planning (2023.findings-acl)
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| Challenge: | Goal-oriented generative script learning aims to generate subsequent steps to reach a specific goal . ability to capture historical states in visual modalities provides detailed information not covered by text . |
| Approach: | They propose a goal-oriented generative script learning task to generate subsequent steps by tracking historical states in both text and vision modalities. |
| Outcome: | The proposed task outperforms baselines in three aspects of the current task. |
The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination (2025.findings-acl)
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Yuji Zhang, Sha Li, Cheng Qian, Jiateng Liu, Pengfei Yu, Chi Han, Yi R. Fung, Kathleen McKeown, ChengXiang Zhai, Manling Li, Heng Ji
| Challenge: | Hallucination is a persistent challenge in large language models where even with rigorous quality control, models often generate distorted facts. |
| Approach: | They propose a new framework to quantify factual hallucinations by modeling knowledge overshadowing. |
| Outcome: | The proposed framework improves model factuality on Overshadow (27.9%), MemoTrap (13.1%) and NQ-Swap (18.3%). |
Non-Sequential Graph Script Induction via Multimedia Grounding (2023.acl-long)
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| Challenge: | Existing scripts for everyday tasks are presented in a linear manner, which does not reflect the flexibility displayed by people executing tasks in real life. |
| Approach: | They propose to use loosely aligned videos to train a non-sequential graph script induction task by using a multimodal framework to ground procedural videos to WikiHow textual steps. |
| Outcome: | The proposed model outperforms the WikiHow linear baseline by 48.76% . it can predict future steps given a partial step sequence and generate explicit graph scripts . |
Multilingual Entity, Relation, Event and Human Value Extraction (N19-4)
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| Challenge: | Existing systems that extract knowledge elements from multiple languages and documents do not aggregate knowledge from multiple documents and languages. |
| Approach: | They propose a multilingual knowledge extraction system that performs entity discovery and linking, relation extraction, event extraction, and coreference. |
| Outcome: | The proposed system performs entity discovery and linking, relation extraction, event extraction, and coreference. |