Papers by Zhongyang Li
Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)
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| Challenge: | Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants. |
| Approach: | They propose to leverage external commonsense knowledge about the intent and sentiment of the event to learn distributed representations for structured events from text. |
| Outcome: | The proposed model improves on hard similarity tasks and yields more precise inferences on subsequent events under given contexts. |
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)
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| Challenge: | Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift. |
| Approach: | They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks. |
| Outcome: | The proposed framework outperforms baselines on Chinese and English CCR datasets. |
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation (2025.findings-acl)
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Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Enting Chen, Damien Graux, Andre Melo, Ruofei Lai, Zeren Jiang, Zhongyang Li, Ye Qi, Yang Ren, Dandan Tu, Jeff Z. Pan
| Challenge: | Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers struggle with multi-hop retrieval scenarios. |
| Approach: | They propose a graph expansion mechanism that augments any conventional base retriever and an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. |
| Outcome: | The proposed system achieves state-of-the-art results on three multi-hop question answering datasets while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. |
Learning to Rank for Plausible Plausibility (P19-1)
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| Challenge: | Existing approaches to encoding sentences using contextualized encoders are inconsistent . |
| Approach: | They propose to use a cross entropy log-loss objective to improve plausibility . they propose a margin-based loss leads to a more plausible model of plausability . |
| Outcome: | The proposed loss is intuitively wrong when applied to plausibility tasks . the proposed loss leads to a more plausible model of plausability . |
Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)
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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che
| Challenge: | Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths. |
| Approach: | They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models. |
| Outcome: | The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio. |
EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems (2026.acl-long)
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Zhengyi Zhao, Shubo Zhang, Yiming Du, Bin Liang, Baojun Wang, Zhongyang Li, Binyang Li, Kam-Fai Wong
| Challenge: | Existing dialogue systems process conversational turns in isolation, overlooking event structures that guide natural interactions. |
| Approach: | They propose a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses. |
| Outcome: | Experiments on three dialogue datasets show that the proposed approach produces more natural responses while requiring less computational overhead. |
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs (2024.emnlp-main)
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| Challenge: | In the age of mobile internet, personal information is constantly being generated on smartphones. |
| Approach: | They propose a novel task of crafting personalized agents powered by large language models that leverage a user's smartphone memories to enhance downstream applications with LLM capabilities. |
| Outcome: | The proposed approach improves 10% over the best existing approach on a real-world dataset and improves usability. |
Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)
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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Large language models extract useful information from conversation history to enhance the response in long-term conversations. |
| Approach: | They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation. |
| Outcome: | The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation . |
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder (D19-1)
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| Challenge: | Understanding event and event-centered commonsense reasoning is crucial for natural language processing (NLP). |
| Approach: | They propose a If-Then commonsense reasoning dataset Atomic and an RNN-based Seq2Seq model to facilitate this. |
| Outcome: | The proposed model improves the accuracy and diversity of inferences compared with baseline methods. |
Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training (C18-1)
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| Challenge: | Story generation is a challenging problem in artificial intelligence (AI) . previous work focused on learning statistical models of event sequences from large-scale text corpora . |
| Approach: | They propose to use adversarial training to generate reasonable story endings . their model includes a generator that defines the policy of generating a story ending . |
| Outcome: | The proposed model achieves better performance on the task of Story Cloze Test with an accuracy of 62.6% compared with state-of-the-art baseline methods. |