Papers by Chuanyi Li
Identifying Exaggerated Language (2020.emnlp-main)
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| Challenge: | Recent studies on metaphor and metonymy have focused on hyperbole, but it is a relatively understudied phenomenon in the figurative language processing community. |
| Approach: | They propose to use hyperbole detection to determine whether a sentence is hyperbolic . they also perform statistical and manual analyses of the corpus and address the automatic hyperbola detection task. |
| Outcome: | The proposed dataset consists of 709 hyperbolic sentences with a non-hyperbolic version created by paraphrasing its hyperbolical counterpart. |
Legal Judgment Prediction: A Reflection on the State of the Art (2026.acl-long)
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| Challenge: | Legal Judgment Prediction (LJP) involves predicting judgment outcomes based on fact descriptions of cases. |
| Approach: | They propose to use argument trees to build automated legal judgment prediction systems that are trustworthy and can be used to predict cases. |
| Outcome: | The proposed model outperforms competitors on standard evaluation datasets and enables pluralistic values to be naturally expressed. |
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing (2024.findings-acl)
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Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, Bozhong Tian
| Challenge: | Current benchmarks focus on coarse-grained knowledge, leaving the intricacies of fine-grounded knowledge unexplored. |
| Approach: | They propose a benchmark and dataset specifically designed for FG multimodal entity knowledge editing. |
| Outcome: | The proposed benchmark underscoring the complexity of FG knowledge editing in MLLMs. |
Legal Case Retrieval: A Survey of the State of the Art (2024.acl-long)
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| Challenge: | Recent years have seen increasing attention on Legal Case Retrieval (LCR) this task involves retrieving cases from a legal database of historical cases that are similar to a given query case. |
| Approach: | They present a survey of the major milestones made in legal case retrieval research . they seek to understand the datasets and recent neural models and their performances . |
| Outcome: | The proposed task is based on a dataset of historical cases similar to a given query case. |
SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM Unlearning (2026.acl-long)
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| Challenge: | Existing approaches focus on general utility metrics, overlooking the preservation of semantically related concepts. |
| Approach: | They propose a method that introduces self-generated proximal visual tokens to prevent forgetting vulnerability. |
| Outcome: | The proposed framework outperforms existing methods in preserving semantically related concepts while achieving effective target unlearning. |
LJPCheck: Functional Tests for Legal Judgment Prediction (2024.findings-acl)
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| Challenge: | Existing LJP models fail to evaluate specific aspects of their performance, such as legal fairness and judicial fairness. |
| Approach: | They propose a suite of functional tests for LJP models to comprehend LJp models’ behaviors and offer diagnostic insights. |
| Outcome: | Extensive tests reveal weaknesses in LJP models and provide diagnostic insights. |
Forget the Token and Pixel: Rethinking Gradient Ascent for Concept Unlearning in Multimodal Generative Models (2025.findings-acl)
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Jiaqi Li, Chuanyi Zhang, Miaozeng Du, Hui Zhang, Yongrui Chen, Qianshan Wei, Junfeng Fang, Ruipeng Wang, Sheng Bi, Guilin Qi
| Challenge: | Gradient Ascent (GA) has emerged as a promising approach for concept unlearning in Multimodal Generative Models (MGMs). |
| Approach: | They propose a novel approach that selectively applies GA to targeted Conceptual Knowledge while preserving Natural Knowledge through Gradient Descent (GD). |
| Outcome: | The proposed approach removes Conceptual Knowledge and inadvertently diminishes Natural Knowledge, resulting in utility degradation. |
Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (2021.findings-emnlp)
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| Challenge: | a method for user targeting is developed to identify online users to whom an ad should be targeted. |
| Approach: | They propose a method for automatic augmentation of positive and negative clickthrough data for user targeting models. |
| Outcome: | The proposed method can increase positive and negative instances of positive training instances on two datasets. |
Open-World Attribute Mining for E-Commerce Products with Multimodal Self-Correction Instruction Tuning (2025.acl-long)
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| Challenge: | Current AM methods focus on extracting attributes from unimodal text, underutilizing multimodal data. |
| Approach: | They propose a framework for multimodal self-correction instruction tuning to extract new attributes from images and text with Multimodal Large Language Models. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two datasets. |
Legal Judgment Prediction via Event Extraction with Constraints (2022.acl-long)
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| Challenge: | Existing models fail to locate key event information that determines the judgment results. |
| Approach: | They propose an Event-based Prediction Model with constraints that exploits constraints in LJP. |
| Outcome: | The proposed model surpasses existing models on a standard LJP dataset in English and French. |
Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for event extraction ignore motion representations in videos and are misguided by background noise. |
| Approach: | They propose a text-video based multimodal event extraction framework that integrates video appearance features and motion representations with video appearance. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in the event extraction field. |
CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset (2024.findings-acl)
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| Challenge: | Legal Judgment Prediction (LJP) has attracted significant attention in recent years. |
| Approach: | They propose a large-scale Chinese Multi-Defendant LJP dataset . they propose case-level evaluation metrics dedicated for the multi-defendant scenario . |
| Outcome: | The proposed methods show weaknesses when applied to cases involving multiple defendants. |
Multimodal Neural Machine Translation: A Survey of the State of the Art (2025.emnlp-main)
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| Challenge: | Multimodal neural machine translation (MNMT) is a task that aims to translate text into the target language using neural networks. |
| Approach: | They propose to integrate other modalities with textual data to enhance translation performance. |
| Outcome: | The proposed task aims to integrate visual modality with textual data to improve translation quality. |