activity
20182022
most citedCSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization

4 citations · 8 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL20221 cited

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…

cs.CL20214 cited

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…

cs.CL20212 cited

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…

cs.CL2019

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…

cs.LG2019

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…

cs.CL20191 cited

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…