Papers by Minping Chen
Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions (2025.emnlp-industry)
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| Challenge: | Existing talent search approaches fail to capture nuanced job-specific preferences and mitigate noise from subjective human judgments. |
| Approach: | They propose a framework that extracts fine-grained recruitment signals from job descriptions and historical hiring data and employs a role-aware multi-gate MoE network to capture behavioral differences across recruiter roles. |
| Outcome: | The proposed framework improves talent search effectiveness and delivers substantial business value. |
Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation (2026.acl-industry)
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| Challenge: | Existing methods to measure the matching degree of a job and a candidate face several challenges, such as low-quality job descriptions and similar candidate-job pairs. |
| Approach: | They propose a large language model-based method that polishes and rewrites low-quality job descriptions by leveraging chain-of-thought prompts and category-aware Mixture of Experts (MoE) module incorporates category embeddings to dynamically assign weights to the experts and learns more distinguishable patterns for similar candidate-job pairs. |
| Outcome: | The proposed method surpasses existing methods by 2.40% in AUC and 7.46% in GAUC and boosts click-through conversion rate (CTCVR) by 19.4% in online tests, saving millions of CNY in external headhunting expenses. |
Exploiting Careful Design of SVM Solution for Aspect-term Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Aspect-term sentiment analysis (ATSA) identifies fine-grained sentiments towards specific aspects of text. |
| Approach: | They propose a pipeline to predict fine-grained sentiments for specific aspects of text . it decomposes the learning problem into multiple view subproblems and dynamically selects and constructs features with reinforcement learning. |
| Outcome: | The proposed pipeline surpasses SVM-based methods in predictive accuracy while maintaining a faster inference speed and significantly reducing the number of model parameters. |
SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing studies focus on learning the joint representation of multiple modalities, ignoring useful knowledge contained in language modal. |
| Approach: | They propose to incorporate sentimental words knowledge into the fusion network to guide the learning of joint representation of multimodal features. |
| Outcome: | The proposed method improves the fusion representation of multimodal features on a YouTube and video dataset. |
Efficient Hyperparameter Optimization for LLM Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing hyperparameter optimization methods are inefficient in reinforcement learning due to model scale and resource-intensive training cycles. |
| Approach: | They propose a hyperparameter optimization method that adapts both model size and training budget as fidelity. |
| Outcome: | The proposed method significantly improves the computational efficiency of each trial (up to 14.9) over existing HPO methods. |