Papers by Yunyao Li
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Existing methods to generate knowledge graphs are unable to handle non-English textual information. |
| Approach: | They propose a task of automatic Knowledge Graph Completion to bridge the gap between English and non-English textual information. |
| Outcome: | The proposed method bridges the gap between the quantity and quality of textual information between English and non-English languages. |
A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels (2020.findings-emnlp)
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| Challenge: | Prior attempts to develop crowdsourcing methods have either had low accuracy or required substantial expert annotation. |
| Approach: | They propose a multi-stage crowd workflow that reduces expert involvement without sacrificing accuracy. |
| Outcome: | The proposed method reduces expert effort by 4x, from 56% to 14% of cases. |
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Existing methods for training one model on multiple languages outperform monolingual baselines for low resource languages. |
| Approach: | They propose a method to combine training data from multiple languages to create a shared representation space for the model. |
| Outcome: | The proposed method outperforms monolingual and polyglot training on low resource languages. |
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs (2024.emnlp-main)
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| Challenge: | XC-Translate is a large-scale, manually-created benchmark for machine translation . current systems struggle to translate texts containing entity names, but KG-MT outperforms state-of-the-art approaches . |
| Approach: | They propose a method to integrate multilingual knowledge into a neural machine translation model . XC-Translate is the first large-scale, manually-created benchmark for machine translation . they propose KG-MT to integrate cultural-related references into MT models . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by a large margin compared to NLLB-200 and GPT-4 . the proposed method is based on a multilingual knowledge graph and dense retrieval mechanism . |
Doc-React: Multi-page Heterogeneous Document Question-answering (2025.acl-short)
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Junda Wu, Yu Xia, Tong Yu, Xiang Chen, Sai Sree Harsha, Akash V Maharaj, Ruiyi Zhang, Victor Bursztyn, Sungchul Kim, Ryan A. Rossi, Julian McAuley, Yunyao Li, Ritwik Sinha
| Challenge: | Existing methods for integrating information across multiple modalities are suboptimal for multi-page, multimodal documents. |
| Approach: | They propose an adaptive iterative framework that balances information gain and uncertainty reduction at each step. |
| Outcome: | The proposed framework captures relevant multimodal content and achieves strong performance on complex QA tasks. |
Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification (2020.emnlp-main)
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Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy
| Challenge: | Existing approaches to explain models are difficult to interpret and have undesirable biases. |
| Approach: | They propose a neural network architecture for learning transparent sentences . they use linguistic expressions built on top of predicates extracted using shallow natural language understanding . |
| Outcome: | The proposed model outperforms statistical relational learning and other neuro-symbolic methods and performs better than black-box recurrent neural networks. |
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization (2020.coling-main)
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Qiuhao Lu, Nisansa de Silva, Dejing Dou, Thien Huu Nguyen, Prithviraj Sen, Berthold Reinwald, Yunyao Li
| Challenge: | Existing graph autoencoders and its variants have been used for node embedding . a new method is proposed to model consistency across different views of networks . |
| Approach: | They propose a network embedding method which enforces latent representations to be consistent across different views of networks by incorporating a multiview adversarial regularization module. |
| Outcome: | The proposed method compares favorably with the state-of-the-art methods on benchmark datasets and on a real-world application. |
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
ACL 2026 Industry Track: Overview (2026.acl-industry)
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| Challenge: | 153 papers were selected for presentation at the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026). |
| Approach: | the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) organised a dedicated industry track. authors acknowledge the challenges in adapting language technologies for building novel and robust. |
| Outcome: | the industry track attracted 532 papers at the 64th Annual Meeting of the Association for Computational Linguistics . the submissions can be grouped into six different clusters: 1. RAG systems & enterprise knowledge AI; 2. agentic systems and workflows; 3. Language technologies and their applications are an integral and critical part of our daily lives. |
Universal Proposition Bank 2.0 (2022.lrec-1)
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Ishan Jindal, Alexandre Rademaker, Michał Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li
| Challenge: | Semantic role labeling (SRL) is a shallow semantic parsing task that identifies "who did what to whom when, where etc." SRL is useful in a wide range of downstream NLP tasks and real-world applications. |
| Approach: | They propose a method to generate shallow semantic parsing tasks using monolingual SRL and multilingual parallel data. |
| Outcome: | The proposed method improves the quality of the generated propbanks. |
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge (2023.emnlp-demo)
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Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang, Anton Belyy, Samira Khorshidi, Fei Wu, Ihab Ilyas, Yunyao Li
| Challenge: | Existing large language models (LLMs) have a tendency to hallucinate and provide creative and fluent responses that are not factually accurate. |
| Approach: | They propose a tool that automatically extracts factual claims from text, gathers evidence from external knowledge sources, evaluates the factuality of each claim, and suggests revisions for identified errors. |
| Outcome: | The proposed tool detects errors in text and evaluates their factuality and suggests revisions based on the collected evidence. |
StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children’s Story-Based Learning (2024.emnlp-main)
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Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun
| Challenge: | Existing story reading systems fail to capture the nuances of how education experts think when conducting interactive story reading activities. |
| Approach: | They propose to use existing question-answering (QA) datasets to capture experts' annotations and thinking process to construct a story-based annotation framework. |
| Outcome: | The proposed framework captures experts’ annotations and thinking process and can be used to generate 5, 868 expert-annotated QA pairs with real-world knowledge. |
Learning Structured Representations of Entity Names using Active Learning and Weak Supervision (2020.emnlp-main)
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| Challenge: | Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. |
| Approach: | They propose a framework that combines active learning and weak supervision to solve this problem. |
| Outcome: | The proposed framework enables learning of high-quality models from a dozen labeled examples. |
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)
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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li
| Challenge: | Existing studies have shown that combining information from KGs in different languages aids knowledge Graph Completion and Knowledge Graph Enhancement. |
| Approach: | They propose a sequence-to-sequence framework that unifies tasks of textual and relational information completion for multilingual knowledge graphs. |
| Outcome: | The proposed framework unifies tasks of KGC and KGE into a single framework. |
SystemT: Declarative Text Understanding for Enterprise (N18-3)
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| Challenge: | a growing number of enterprise applications are relying on text understanding systems to understand information in unstructured and semi-structured forms. |
| Approach: | They propose a declarative text understanding system that addresses these challenges . they summarize the impact of SystemT on business and education . |
| Outcome: | The system addresses the challenges of enterprise text understanding systems . it has been deployed in a wide range of enterprise applications . |
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)
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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
| Challenge: | Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point. |
| Approach: | They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully. |
| Outcome: | The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations. |
Semantic-Aware Logical Reasoning via a Semiotic Framework (2026.acl-long)
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Yunyao Zhang, Xinglang Zhang, Junxi Sheng, Wenbing Li, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang, Zikai Song
| Challenge: | Existing studies largely overlook the interplay between logical complexity and semantic complexity, limiting their robustness under abstract propositions, ambiguous contexts, and conflicting stances. |
| Approach: | They propose a semiotic-square-guided framework that integrates automated deduction with reflective verification to manage logical complexity across deeper reasoning chains. |
| Outcome: | The proposed framework achieves state-of-the-art performance on RepublicQA with 6.25% average gain, and generalizes well to four mainstream logical reasoning benchmarks with an additional 7.05% improvement. |
Label Definitions Improve Semantic Role Labeling (2022.naacl-main)
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| Challenge: | Existing work on semantic role labeling treats symbolic labels as symbolic . labeled data is costly and often lacking in many tasks, domains, and languages. |
| Approach: | They propose to retrieve and leverage semantic role labels from annotation guidelines . argument classification is at the core of Semantic Role Labeling . |
| Outcome: | The proposed model achieves state-of-the-art on a CoNLL09 dataset injected with label definitions given the predicate senses. |
Time Sensitive Knowledge Editing through Efficient Finetuning (2024.acl-short)
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Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, Yunyao Li
| Challenge: | Existing locate-and-edit knowledge editing methods suffer from two limitations: they are infeasible for large scale KE in practice and require long run-time. |
| Approach: | They propose to use parametric fine-tuning techniques to update obsolete knowledge and induce new knowledge into LLMs. |
| Outcome: | The proposed methods improve the performance of KE and knowledge update in a temporal dataset with knowledge update and knowledge injection examples. |
Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)
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| Challenge: | Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks. |
| Approach: | This tutorial will cover applying deep learning on graph techniques to NLP using Graph Neural Networks (GNNs) Graph4NLP is the first library for researchers and practitioners for easy use of GNNs for various NLP tasks. |
| Outcome: | This tutorial will cover the latest developments in deep learning on graph techniques and their applications in various NLP tasks. |
Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Domain-Aware Dependency Parsing for Questions (2021.findings-acl)
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| Challenge: | Pre-trained parsers perform poorly on domain-specific questions, a paper argues . retraining parser with domain- specific questions is expensive, as these require linguistic expertise. |
| Approach: | They propose an automatic labeled domain question generation framework leveraging domain knowledge and seed domain questions. |
| Outcome: | The proposed framework improves state-of-the-art parsers on domain questions. |
RETAIN: Interactive Tool for Regression Testing Guided LLM Migration (2024.emnlp-demo)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into diverse applications. |
| Approach: | They propose a tool specifically designed for regression testing during LLM migrations. |
| Outcome: | RETAIN (REgression Testing guided LLM migrAtIoN) provides a tool specifically designed for regression testing during LLM migrations. |
HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop (P19-3)
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| Challenge: | HITL-ML approaches are too low-level and far-removed from human’s conceptual models. |
| Approach: | They propose a prototype HITL-ML system that exposes the machine-learned model through high-level, explainable linguistic expressions formed of predicates representing semantic structure of text. |
| Outcome: | The proposed system exposes the machine-learned model through high-level, explainable linguistic expressions formed of predicates representing semantic structure of text. |
Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation (2024.lrec-main)
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Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
| Challenge: | Prior studies have shown that sequence-to-sequence models learn to hallucinate when the conditioning data has poor correlation with the sequence being produced. |
| Approach: | They construct a dataset that pairs Knowledge Graphs (KG) and text together and compare their results to a cyclic evaluation model. |
| Outcome: | The proposed model performs better on cyclic generation of KGs than on KG-T, but less well on synchronization of KTs. |
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)
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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
| Challenge: | Existing work deals with EL in the context of longer text, such as a sentence. |
| Approach: | They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches. |
| Outcome: | The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods. |
PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation (2023.findings-eacl)
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Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran, Huaiyu Zhu, Hiroshi Kanayama, Marina Danilevsky, Yunyao Li
| Challenge: | Existing evaluation scripts for semantic role labeling do not consider error propagation . existing evaluation script does not consider argument independent of predicate sense . |
| Approach: | They propose a more strict SRL evaluation metric PriMeSRL to address these issues . they propose to use a metric that measures the quality of the underlying SRL models . |
| Outcome: | The proposed metric reduces quality evaluation of all SoTA SRL models and penalizes failures. |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large Language Models (2024.emnlp-industry)
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Ronak Pradeep, Daniel Lee, Ali Mousavi, Jeffrey Pound, Yisi Sang, Jimmy Lin, Ihab Ilyas, Saloni Potdar, Mostafa Arefiyan, Yunyao Li
| Challenge: | Knowledge Graphs (KGs) are a powerful tool for capturing structured representations of the world. |
| Approach: | They propose a scalable method for generating up-to-date and configurable conversational KGQA datasets that adheres to human interaction configurations and operates at a significantly larger scale. |
| Outcome: | Qualitative psychometric analyses show that ConvKGYarn produces high-quality data comparable to popular conversational KGQA datasets across various metrics. |
Entity Disambiguation via Fusion Entity Decoding (2024.naacl-long)
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Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander Rush, Umar Farooq Minhas, Yunyao Li
| Challenge: | Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark. |
| Approach: | They propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. |
| Outcome: | The proposed model outperforms existing classification models on the ZELDA benchmark and on retrieval/reader frameworks. |
Development of an Enterprise-Grade Contract Understanding System (2021.naacl-industry)
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Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
| Challenge: | Currently, legal contract review remains an expensive and arduous process. |
| Approach: | They describe a commercial system designed and deployed for contract understanding that enables legal professionals to review contracts. |
| Outcome: | The proposed system is used by a wide range of enterprise users and solves three major challenges. |
ACL 2025 Industry Track: Overview (2025.acl-industry)
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| Challenge: | 108 papers were selected for presentation at the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025). |
| Approach: | 108 papers were selected for presentation at the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025). |
| Outcome: | The industry track attracted 421 paper submissions at the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025). |
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications (2023.acl-long)
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| Challenge: | Existing consensus on which OpenIE model is best for each application is lacking . different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate OpenIE system for one’s applications. |
| Approach: | They propose to use OpenIE to extract relation tuples from plain text to compare different models and training sets to find the best model for their applications. |
| Outcome: | The proposed models perform well on a Complex QA application. |
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering (2025.coling-demos)
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| Challenge: | Recent advances in retrieval-augmented generation have demonstrated impressive performance on the question-answering task. |
| Approach: | They propose a retrieval-augmented generation framework that generates an initial text answer and retrieves multimodal data relevant to the snippets of the initial text. |
| Outcome: | The proposed framework can be easily integrated into an enterprise chatbot to produce multimodal answers with minimal modifications. |
Federated Retrieval Augmented Generation for Multi-Product Question Answering (2025.coling-industry)
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| Challenge: | Existing multi-domain RAG-QA approaches query all domains indiscriminately or rely on rigid resource selection. |
| Approach: | They propose a multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge. |
| Outcome: | The proposed framework improves multi-product knowledge-augmented QA performance on Adobe products. |
Low-resource Deep Entity Resolution with Transfer and Active Learning (P19-1)
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| Challenge: | Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. |
| Approach: | They propose a deep learning-based method that targets low-resource settings for ER by combining transfer learning and active learning. |
| Outcome: | The proposed method achieves comparable, if not better, performance compared to state-of-the-art learning-based methods while using an order of magnitude fewer labels. |
Exploiting Structure in Representation of Named Entities using Active Learning (C18-1)
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| Challenge: | Named entities are atomic objects of reference and reasoning in many knowledge-centric applications. |
| Approach: | They propose an active-learning based framework that drastically reduces the labeled data required to learn entities' structures. |
| Outcome: | The proposed framework outperforms handwritten programs and supervised learning models in relation extraction and entity resolution tasks. |
Small but Mighty: New Benchmarks for Split and Rephrase (2020.emnlp-main)
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| Challenge: | Split and Rephrase is a text simplification task that requires a strong evaluation benchmark and metric . despite its relatively new nature, the benchmark dataset contains easily exploitable syntactic cues . |
| Approach: | They propose to use crowdsourced datasets to evaluate split and rephrase models . they find that the widely used benchmark dataset universally contains exploitable syntactic cues . |
| Outcome: | The proposed model performs better than the state-of-the-art model, the authors say . they show that the datasets contain significantly more diverse syntax . |
AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings (2024.emnlp-main)
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| Challenge: | Existing ranking algorithms restrict granularity to full passages or require a specific dense index for each desired level of granules. |
| Approach: | They propose a multi-vector ranking approach that leverages multi-vctor embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of grail. |
| Outcome: | The proposed method surpasses prompt-driven citation generation by incorporating proposition-level ranking to post-hoc citation addition. |
Domain Representative Keywords Selection: A Probabilistic Approach (2022.findings-acl)
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| Challenge: | a probabilistic approach to select a subset of a target domain representative keywords is crucial for many downstream tasks in natural language processing. |
| Approach: | They propose a probabilistic approach to select a subset of a target domain representative keywords from a candidate set, contrasting with a context domain. |
| Outcome: | The proposed approach provides more importance to distinctive keywords than common keywords contrasting with the context domain. |