Papers by Min Cao
An Empirical Study of Frame Selection for Text-to-Video Retrieval (2023.findings-emnlp)
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| Challenge: | Existing methods for text-to-video retrieval select a subset of frames to represent video content . current methods only explore video contents while ignoring relevancy to texts . |
| Approach: | They propose to use a subset of frames to represent video content for TVR . they analyze six different frame selection methods to determine their effectiveness . |
| Outcome: | The proposed method improves retrieval efficiency without sacrificing visual details . the proposed method explores the video contents while ignoring relevancy to texts . |
Dynamic and Efficient Inference for Text Generation via BERT Family (2023.acl-long)
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| Challenge: | Existing methods to improve performance of pre-trained language models are limited due to large-scale parameters and the universal autoregressive decoding paradigm. |
| Approach: | They propose a novel fine-tuning method which can make a single pre-trained model support Dynamic and Efficient infERence and achieve an adaptive trade-off between model performance and latency. |
| Outcome: | The proposed method achieves higher BLEU scores than the strong autoregressive Transformer model on translation tasks with 3 12 times speedup and faster inference speed compared with the BART model on four GLGE benchmark tasks. |
Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)
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| Challenge: | Existing models for text generation use a discrete data embedding module to map the data into the continuous space. |
| Approach: | They propose two methods to bridge the gap between training and inference by mapping the discrete text into the continuous space. |
| Outcome: | The proposed methods can achieve 100 200 speedup with better performance on 6 generation tasks. |
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2022.findings-emnlp)
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Zefeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li
| Challenge: | Extensive experiments show that STAR outperforms previous pre-training methods and ranks first on the leaderboard . text-to-SQL parsing aims to translate natural language (NL) questions into executable SQL queries . |
| Approach: | They propose a SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing . they propose two objectives that explore context-dependence of NL utterances and SQL queries . |
| Outcome: | The proposed framework outperforms existing methods on two downstream benchmarks and ranks first on the leaderboard. |
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking (2024.eacl-long)
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| Challenge: | Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. |
| Approach: | They propose a framework to integrate Chinese geographic semantics into re-ranking pipelines. |
| Outcome: | The proposed framework improves on two Chinese benchmark datasets. |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)
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| Challenge: | Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task. |
| Approach: | They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision. |
| Outcome: | The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods . |
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction (2024.findings-acl)
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| Challenge: | Existing studies on IE tasks have focused on recognizing and analyzing cross-modal information . a multimodal large language model (MLLM) is developed to analyze IE across modalities . |
| Approach: | They propose a multimodal large language model (MLLM) capable of grounding information from all modalities. |
| Outcome: | The proposed framework provides a framework to analyze IE tasks over various modalities and their fine-grained groundings. |
Learning to Describe Implicit Changes: Noise-robust Pre-training for Image Difference Captioning (2025.findings-emnlp)
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Zixin Guo, Jiayang Sun, Tzu-Jui Julius Wang, Abduljalil Radman, Selen Pehlivan, Min Cao, Jorma Laaksonen
| Challenge: | Large Multimodal Models (LMMs) are used to capture subtle differences between images but are noisy and coarse summaries. |
| Approach: | They propose a noise-robust approach to image difference capture using large multimodal models . they use LMMs with structured prompts to generate fine-grained change descriptions . |
| Outcome: | The proposed model outperforms streamlined architectures and improves inference efficiency. |
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)
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Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su
| Challenge: | Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains. |
| Approach: | They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore. |
| Outcome: | The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore. |
Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval (2024.findings-acl)
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| Challenge: | Existing models for non-parametric domain adaptation lack kNN retrieval at each timestep, leading to substantial time overhead. |
| Approach: | They propose a kNN-MT-based model that uses a domain-specific translation knowledge store to interpolate the prediction distribution of the model. |
| Outcome: | The proposed model significantly extends kNN-MT with dynamic retrieval on widely-used datasets. |
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)
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Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang
| Challenge: | Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment. |
| Approach: | They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data . |
| Outcome: | The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech. |
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding (2022.coling-1)
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| Challenge: | Existing methods for dialog understanding only consider self-augmented dialogs as positive samples and treat all other dialogs like negative ones. |
| Approach: | They propose a tree-structured pre-trained conversation model which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training. |
| Outcome: | The proposed model can achieve state-of-the-art results on the DialoGLUE benchmark. |
Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys (2024.findings-eacl)
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| Challenge: | integrating cultural dimensions with dialogue encoding features can enhance the predictive accuracy and quality of dialogue agents. |
| Approach: | They propose to incorporate cultural dimensions into dialogue encoding features to enhance the predictive accuracy of dialogue agents. |
| Outcome: | The proposed model improves the accuracy and quality of dialogue predictions by incorporating cultural dimensions with dialogue encoding features. |
Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing (2025.acl-long)
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Longhui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao, Ensheng Shi, Mayuchi Mayuchi, Jun Yu, Honghai Liu, Jing Li, Min Zhang
| Challenge: | Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns. |
| Approach: | They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities. |
| Outcome: | The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages. |
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval (2024.emnlp-main)
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Weiwei Sun, Zhengliang Shi, Wu Long, Lingyong Yan, Xinyu Ma, Yiding Liu, Min Cao, Dawei Yin, Zhaochun Ren
| Challenge: | Existing IR benchmarks focus on a limited scope of tasks, making them insufficient for evaluating the latest IR models. |
| Approach: | They propose a multi-task instruction-tuned IR benchmark that includes 126 distinct IR tasks across 6 domains. |
| Outcome: | The proposed model performs better on instruction-tuned models than non-instruction-tunned models on MAIR. |