Papers by Zujie Wen

A Dialogue-based Information Extraction System for Medical Insurance Assessment (2021.findings-acl)

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Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu

Dialogue Systems & Conversational Agents

Challenge:	a new system that integrates advanced NLP technologies for medical insurance assessment is proposed . the average time cost of the procedure is reduced from 55 minutes to 35 minutes .
Approach:	They propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment.
Outcome:	The proposed system reduces the time cost of the procedure from 55 minutes to 35 minutes and saves 30% human resources cost compared with the previous offline procedure.

Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards (2026.acl-long)

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Xinyu Tang, Yuliang Zhan, Zhixun Li, Xin Zhao, Zhenduo Zhang, Zujie Wen, Zhiqiang Zhang, Jun Zhou

Reasoning

Challenge:	Large reasoning models are typically trained using reinforcement learning with verifiable reward (RLVR) positive and negative self-generated rollouts are used to update the model's policy . positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths.
Approach:	They propose a method that allocates advantage signals to key tokens across different polarities.
Outcome:	The proposed method improves the ability of large reasoning models to learn from their own generated rollouts.

SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (2022.findings-naacl)

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Kuan Xu, Yongbo Wang, Yongliang Wang, Zihao Wang, Zujie Wen, Yang Dong

Programming Languages in NLP

Challenge:	Using sketch-based slot filling, text-to-SQL models suffer from over-complexity . et al., e.al., and d.albert, dr., propose a novel method for text- to-Sql generation .
Approach:	They propose to train sequence-to-sequence model with Schema-aware Denoising . they propose a clause-sensitive execution guided (EG) decoding strategy .
Outcome:	The proposed method improves performance in schema linking and grammar correctness . it also establishes new state-of-the-art on the WikiSQL benchmark .

R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling (2021.acl-long)

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Xiang Hu, Haitao Mi, Zujie Wen, Yafang Wang, Yi Su, Jing Zheng, Gerard de Melo

Language Models

Challenge:	Existing models with stacked layers do not explicitly model hierarchical structure of language understanding.
Approach:	They propose a recursive Transformer model based on differentiable CKY style binary trees to emulate hierarchical composition process.
Outcome:	The proposed model can predict words given their left and right abstraction nodes.

Improve Interpretability of Neural Networks via Sparse Contrastive Coding (2022.findings-emnlp)

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Junhong Liu, Yijie Lin, Liang Jiang, Jia Liu, Zujie Wen, Xi Peng

Explainability & Interpretability in NLP

Challenge:	XAI has achieved remarkable advances, but few efforts have been devoted to solving the problem.
Approach:	They propose a model-agnostic explanation method termed Sparse Contrastive Coding . they use model-based explanations to explain the black-box in a more model-oriented way .
Outcome:	The proposed method outperforms five state-of-the-art methods in interpretability and classification metrics.

Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)

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Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua

Natural Language Interfaces

Challenge:	Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users.
Approach:	They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives.
Outcome:	The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives.

Query Distillation: BERT-based Distillation for Ensemble Ranking (2020.coling-industry)

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Wangshu Zhang, Junhong Liu, Zujie Wen, Yafang Wang, Gerard de Melo

Language Models

Challenge:	Recent years have witnessed substantial progress in the development of neural ranking networks, but an increasingly heavy computational burden due to growing numbers of parameters and the adoption of model ensembles.
Approach:	They propose a two-stage distillation method that allows a smaller student model to be trained while benefiting from the better performance of the teacher model.
Outcome:	The proposed method shows higher-quality rankings compared to the teacher model.

Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals (2023.findings-acl)

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Hongru Liang, Jia Liu, Weihong Du, Dingnan Jin, Wenqiang Lei, Zujie Wen, Jiancheng Lv

Natural Language Interfaces

Challenge:	Existing methods for machine reading comprehension of user manuals have trouble answering complex questions.
Approach:	They propose a knowing-how & knowing-that task that requires the model to answer factoid-style, procedure-style and inconsistent questions about user manuals.
Outcome:	The proposed model can answer factoid-style, procedure-style and inconsistent questions about user manuals.

Towards Effective Automatic Debt Collection with Persona Awareness (2023.emnlp-industry)

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Tong Zhang, Junhong Liu, Chen Huang, Jia Liu, Hongru Liang, Zujie Wen, Wenqiang Lei

Natural Language Interfaces

Challenge:	Existing debt collection agents fail to tailor strategies to debtor personas, leading to ineffective collection.
Approach:	They present a commercial practice on debt collection agents that organizes debtor personas into a taxonomy and constructs a persona-aware conversation dataset.
Outcome:	The proposed agent increases recovery rate by 3.31% and collects additional 100K RMB after two months of testing.

CARE: A Clue-guided Assistant for CSRs to Read User Manuals (2024.acl-long)

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Weihong Du, Jia Liu, Zujie Wen, Dingnan Jin, Hongru Liang, Wenqiang Lei

Challenge:	Current solutions don't fit the online custom service scenarios well due to the lack of attention to user questions and possible responses.
Approach:	They propose to build a clue-guided assistant for customer service representations (CSRs) that can provide accurate responses and explicitly show explainable paths about how to arrive at these responses.
Outcome:	The proposed assistant can reduce CSRs' reading burden and keep high service quality, in particular with >35% decrease in time spent and keeping a >0.75 ICC score.

Interactive Question Clarification in Dialogue via Reinforcement Learning (2020.coling-industry)

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Xiang Hu, Zujie Wen, Yafang Wang, Xiaolong Li, Gerard de Melo

Natural Language Interfaces

Challenge:	ambiguous questions are a perennial problem in real-world dialogue systems.
Approach:	They propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query.
Outcome:	The proposed model improves on real-world user clicks and shows significant improvements . it suggests that the original query is refined to clarify ambiguous questions .