160 citations · 178 across the 14 of their papers we have counts for
14 papers
TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification
Junnan Zhu, Min Xiao, Yining Wang +3
LLMs have achieved remarkable fluency and coherence in text generation, yet their widespread adoption has raised concerns about content reliability and accountability. In high-stak…
SimulPL: Aligning Human Preferences in Simultaneous Machine Translation
Donglei Yu, Yang Zhao, Jie Zhu +3
Simultaneous Machine Translation (SiMT) generates translations while receiving streaming source inputs. This requires the SiMT model to learn a read/write policy, deciding when to…
Self-Modifying State Modeling for Simultaneous Machine Translation
Donglei Yu, Xiaomian Kang, Yuchen Liu +2
Simultaneous Machine Translation (SiMT) generates target outputs while receiving stream source inputs and requires a read/write policy to decide whether to wait for the next source…
CFSum: A Coarse-to-Fine Contribution Network for Multimodal Summarization
Min Xiao, Junnan Zhu, Haitao Lin +2
Multimodal summarization usually suffers from the problem that the contribution of the visual modality is unclear. Existing multimodal summarization approaches focus on designing t…
Multi-Teacher Knowledge Distillation For Text Image Machine Translation
Cong Ma, Yaping Zhang, Mei Tu +3
Text image machine translation (TIMT) has been widely used in various real-world applications, which translates source language texts in images into another target language sentenc…
E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation
Cong Ma, Yaping Zhang, Mei Tu +3
Text image machine translation (TIMT) aims to translate texts embedded in images from one source language to another target language. Existing methods, both two-stage cascade and o…
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…
Graph Neural Networks: Taxonomy, Advances and Trends
Yu Zhou, Haixia Zheng, Xin Huang +3
Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys…
NCLS: Neural Cross-Lingual Summarization
Junnan Zhu, Qian Wang, Yining Wang +4
Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language. Existing methods simply divide this tas…
Understanding Memory Modules on Learning Simple Algorithms
Kexin Wang, Yu Zhou, Shaonan Wang +2
Recent work has shown that memory modules are crucial for the generalization ability of neural networks on learning simple algorithms. However, we still have little understanding o…
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference
He Bai, Yu Zhou, Jiajun Zhang +1
Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations. However, most of the pre…
Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
He Bai, Yu Zhou, Jiajun Zhang +3
To deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. Trans…