Shuqin Li — accepted AI-conference papers

2025

SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification

Han Liu, Shuqin Li, Xiaotong Zhang, Yuanyuan Wang, Feng Zhang, Hongyang Chen +1

NAACL 2025findings

Multi-label document classification (MLDC) aims to allocate more than one label to each document and attracts increasing attention in many practical applications. However, previous studies have failed to pay sufficient attention to the lack of semantic information on labels and the long-tail problem…

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2024

Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue

Huifang Du, Shuqin Li, Minghao Wu, Xuejing Feng, Yuan-Fang Li, Haofen Wang

EMNLP 2024finding

Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems. However, existing RL methods tend to mainly focus on generation tasks, such as dialogue policy learning (DPL) or response generation (RG), while neglecting dialogue state tracking (DST) for understand…

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