Papers by Shuguang Han

7 papers
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)

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Challenge: Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate .
Approach: They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity .
Outcome: The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds.
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets (2023.findings-acl)

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Challenge: Existing work on conversational recommendation systems lacks high-quality data . existing datasets lack large-scale and high-level data based on human annotators .
Approach: They propose an automatic dataset synthesis approach that generates large-scale recommendation dialogues using structured graphs based on user-item information from the real world.
Outcome: The proposed approach can generate large-scale and high-quality recommendation dialogues . it exploits user preferences, knowledge graphs, and conversation ability from existing datasets based on real-world data .
GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)

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Challenge: Structured product information is a major bottleneck for the efficiency of e-commerce platforms.
Approach: They propose a data-driven approach to generate product structured representations using product metadata.
Outcome: Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms.
Don’t Tell the Answer, Truly Guide the Reasoning During RL Rollouts (2026.findings-acl)

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Challenge: Existing methods such as GRPO often break down when task difficulty exceeds the model’s capacity, resulting in sparse rewards and inefficient training.
Approach: They propose to measure the compatibility between external guidance and a model's intrinsic policy by introducing an adaptive framework to enhance reasoning performance while explicitly preserving high Affinity.
Outcome: The proposed framework outperforms baseline models while maintaining high Affinity.
ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models (2026.findings-acl)

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Challenge: Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability.
Approach: They propose a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training.
Outcome: The proposed framework reduces inference cost while maintaining strong reasoning ability across multiple benchmarks.
Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)

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Challenge: Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability.
Approach: They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain.
Outcome: The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset.
IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing (2024.emnlp-industry)

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Challenge: Unlike professional Business-to-Consumer (B2C) e-commerce platforms, consumer-to consumer (C2C), is mainly targeting individual sellers.
Approach: They develop an intelligent product listing tool that generates product descriptions using various product attributes such as category, brand, color, condition, etc.
Outcome: The proposed tool outperforms the base model in domain-specific tasks while producing less hallucination.

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