Papers by Wang Yubin
MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)
Copied to clipboard
Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu
| Challenge: | Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets. |
| Approach: | They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity. |
| Outcome: | Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets. |
GAPO: Robust Advantage Estimation for Real-World Code LLMs (2026.findings-acl)
Copied to clipboard
Jianqing Zhang, Zhezheng Hao, Wei Xia, Hande Dong, Hong Wang, Chenxing Wei, Yuyan Zhou, Yubin Qi, Qiang Lin, Jian Cao
| Challenge: | Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, but in real-world code editing scenarios, reward distributions are often skewed with unpredictable noise, leading to distorted advantage computation and increased rollout outliers. |
| Approach: | They propose a group-relative method that finds an interval with the highest SNR and uses the median of that interval as an adaptive Q to replace the group mean in advantage calculation. |
| Outcome: | The proposed method improves on nine instruction-tuned LLMs while remaining plug-and-play and efficient. |
Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to remove noise from dependency trees are not optimal due to complexity and variability of natural language. |
| Approach: | They propose a dynamically pruned Graph Convolutional Network (DP-GCN) that prunes the dependency tree with rethinking in an end-to-end scheme. |
| Outcome: | The proposed model achieves impressive results compared to strong competitors. |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |
BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation (2021.acl-long)
Copied to clipboard
| Challenge: | citing sentences capture salient information in cited papers and the connection between citing and citing papers. |
| Approach: | They propose a BAckground knowledge- and COntent-based framework for citing sentence generation that integrates two types of information: background knowledge and content. |
| Outcome: | The proposed framework outperforms baselines in the citation sentence generation task. |
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to event detection ignore the trigger discrepancy and cause errors. |
| Approach: | They propose a unified model which converts a few-shot tagging problem into a single-shot model by using a Gaussian distribution. |
| Outcome: | The proposed model performs better than existing identifythen-classify models on a few-shot tagging problem with a double-part taging scheme. |
Is Continuous Prompt a Combination of Discrete Prompts? Towards a Novel View for Interpreting Continuous Prompts (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on the interpretability and transferability of continuous prompts have not been conducted on the subject. |
| Approach: | They propose to interpret continuous prompts as the weighting of discrete prompts by jointly optimizing prompt fidelity and downstream fidelity. |
| Outcome: | The proposed interpretations provide effective readability and plausibility, which is helpful to understand the decision-making of continuous prompts and discover potential shortcuts. |
Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document . |
| Approach: | They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction. |
| Outcome: | The proposed model achieves state-of-the-art performance on two widely used datasets. |
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings (2021.emnlp-main)
Copied to clipboard
Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su
| Challenge: | Existing sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. |
| Approach: | They propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering. |
| Outcome: | The proposed model achieves state-of-the-art performance on five commonly-used datasets. |
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing joint models only use training procedure to determine the implicit correlation between intents and slots. |
| Approach: | They propose to make full use of the statistical co-occurrence frequency between intents and slots as prior knowledge to enhance joint multiple intent detection and slot filling. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public multi-intent datasets. |