4 citations · 17 across the 14 of their papers we have counts for
14 papers
Listening to the Echo: User-Reaction Aware Policy Optimization via Scalar-Verbal Hybrid Reinforcement Learning
Jing Ye, Xinpei Zhao, Lu Xiang +2
While current emotional support dialogue systems typically rely on expert-defined scalar rewards for alignment, these signals suffer from severe information sparsity. They cannot e…
PromptDLA: A Domain-aware Prompt Document Layout Analysis Framework with Descriptive Knowledge as a Cue
Zirui Zhang, Yaping Zhang, Lu Xiang +4
Document Layout Analysis (DLA) is crucial for document artificial intelligence and has recently received increasing attention, resulting in an influx of large-scale public DLA data…
ICDAR 2025 Competition on End-to-End Document Image Machine Translation Towards Complex Layouts
Yaping Zhang, Yupu Liang, Zhiyang Zhang +5
Document Image Machine Translation (DIMT) seeks to translate text embedded in document images from one language to another by jointly modeling both textual content and page layout,…
EmoHarbor: Evaluating Personalized Emotional Support by Simulating the User's Internal World
Jing Ye, Lu Xiang, Yaping Zhang +1
Current evaluation paradigms for emotional support conversations tend to reward generic empathetic responses, yet they fail to assess whether the support is genuinely personalized…
HiSciBench: A Hierarchical Multi-disciplinary Benchmark for Scientific Intelligence from Reading to Discovery
Yaping Zhang, Qixuan Zhang, Xingquan Zhang +8
The rapid advancement of large language models (LLMs) and multimodal foundation models has sparked growing interest in their potential for scientific research. However, scientific…
A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities
Lu Xiang, Yang Zhao, Yaping Zhang +1
Large Language Models (LLMs) have demonstrated their transformative potential across numerous disciplinary studies, reshaping the existing research methodologies and fostering inte…
Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency
Yupu Liang, Yaping Zhang, Zhiyang Zhang +5
Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document…
Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation
Yupu Liang, Yaping Zhang, Zhiyang Zhang +4
Document Image Machine Translation (DIMT) aims to translate text within document images, facing generalization challenges due to limited training data and the complex interplay bet…
From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment
Jing Ye, Lu Xiang, Yaping Zhang +1
Effective emotional support hinges on understanding users' emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great…
SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent
Jing Ye, Lu Xiang, Yaping Zhang +1
Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly form…
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation
Yang Zhao, Junnan Zhu, Lu Xiang +4
A common scenario of Multilingual Neural Machine Translation (MNMT) is that each translation task arrives in a sequential manner, and the training data of previous tasks is unavail…
Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions
Haitao Lin, Junnan Zhu, Lu Xiang +3
Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing e…
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Haitao Lin, Liqun Ma, Junnan Zhu +4
Dialogue summarization has drawn much attention recently. Especially in the customer service domain, agents could use dialogue summaries to help boost their works by quickly knowin…
Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models
Haitao Lin, Lu Xiang, Yu Zhou +2
Spoken Language Understanding (SLU) is one essential step in building a dialogue system. Due to the expensive cost of obtaining the labeled data, SLU suffers from the data scarcity…